Method and apparatus for human behavior modeling in adaptive training

ABSTRACT

A computer implemented method, apparatus, and computer usable program code for simulating human behavior. Source code for predicting human behavior is located on a storage system in a network data processing system. An interpreter executing on hardware in the network data processing system includes a language interpreter and a communications module. The language interpreter executes a simulation with the source code using artificial intelligence to generate a new definition and interpreted source code. A graphical user interface processor receives the interpreted source code from the language interpreter and generates device dependent output. Devices display the device dependent output, receive user input, and send received user input to the graphical user interface processor. The communications module receives user input from the graphical user interface processor and the new definition from the language interpreter and modifies the source code to form modified source code, executed by the language interpreter.

RELATED PROVISIONAL APPLICATION

The present disclosure is related to and claims the benefit of priorityof provisional U.S. patent application Ser. No. 60/892,467 entitled“Method and Apparatus for Human Behavior Modeling in Adaptive Training”,filed on Mar. 01, 2007, which is hereby incorporated by reference.

BACKGROUND INFORMATION

1. Field

The present disclosure provides an improved data processing system andin particular, a method and apparatus for processing data. Still moreparticularly, the present invention relates to a computer implementedmethod, apparatus, and computer usable program code for modeling andsimulating human behavior.

2. Background

Human behavior is a collection of activities performed by human beings.These activities are influenced by factors, such as, for example,culture, attitudes, emotions, values, ethics, authority, persuasion,and/or coercion. The behavior of human beings falls within a range inwhich some behavior is common, some behavior is considered unusual, someother behavior is considered acceptable, and other behavior is outsideof acceptable limits. The behavior of people has been studied by manyacademic disciplines, such as psychology, sociology, and anthropology.More recently, the use of computers have been applied to the study ofhuman behavior.

Additionally, simulations of human behavior have been used performmilitary exercises and planning. Human behavior simulation also may beused with respect to predicting other situations, such as economic andsocial actions. An ability to predict human behavior would be useful indeveloping training programs. Knowing how trainees will respond dodifferent stimuli may be used to develop and modify training programs.

Current models and simulation programs do not properly simulate humanbehavior for different reasons. As an example, currently availablesimulation programs are suited only for a particular type of simulation.As a result, when a different type of simulation is required, a newprogram is required to be written to perform that simulation.Additionally, the number of relations and the ability to modify thoserelations is limited.

Therefore, it would be advantageous to have an improved computerimplemented method, apparatus, and computer usable program code formodeling and simulating human behavior for use in a training program.

SUMMARY

The advantageous embodiments provide a computer implemented method,apparatus, and computer usable program code for managing trainingprograms. Information is identified about a set of trainees for atraining program to form identified information. A set of synthetictrainees is defined using the identified information to form a firstdefinition. The first definition is located in a source code in aframework used to simulate human behavior. A training program is definedto form a second definition. The second definition is located in thesource code for the framework. A simulation is performed on the set ofsynthetic trainees with the training program in the framework using thesource code to obtain results.

In another advantageous embodiment, an apparatus is provided havingsource code located on a storage system in a network data processingsystem. The source code is written in a language for predicting humanbehavior, has a first definition for a set of trainees, and a seconddefinition for a training program. An interpreter executing in thenetwork data processing system is present. The interpreter executes asimulation of the training program using the first definition and thesecond definition in source code to generate interpreted source code forresults of the simulation. A graphical user interface processorexecuting in the network data processing system is also present. Thegraphical user interface processor receives the interpreted source codefrom the interpreter to form received interpreted source code andgenerates device dependent output using the received interpreted sourcecode to present the results. The interpreter uses the interpreted sourcecode to modify the source code to form modified source code for useduring simulation of the training program.

In yet another advantageous embodiment, a computer program producthaving a computer usable medium having computer usable program code formanaging training programs is provided. Computer usable program code ispresent for identifying information about a set of trainees for atraining program to form identified information. Also, computer usableprogram code is present for defining a set of synthetic trainees usingthe identified information to form a first definition. The firstdefinition is located in a source code in a framework used to simulatehuman behavior. Computer usable program code is present for defining atraining program to form a second definition, wherein the seconddefinition is located in the source code for the framework. In addition,computer usable program code for is present performing a simulation onthe set of synthetic trainees with the training program in the frameworkusing the source code to obtain results.

The features, functions, and advantages can be achieved independently invarious embodiments or may be combined in yet other embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself, however, as well asa preferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofan advantageous embodiment when read in conjunction with theaccompanying drawings, wherein:

FIG. 1 is a pictorial representation of a network of data processingsystems in which the advantageous embodiments may be implemented;

FIG. 2 is a diagram of a data processing system in accordance with anadvantageous embodiment;

FIG. 3 is a diagram illustrating a simulation system in accordance withan advantageous embodiment;

FIG. 4 is a diagram of a human behavioral modeling and simulationdevelopment framework in accordance with an advantageous embodiment;

FIG. 5 is a diagram illustrating a distribution of modules in aframework in accordance with an advantageous embodiment;

FIG. 6 is a diagram illustrating a source module code in accordance withan advantageous embodiment;

FIG. 7 is a diagram illustrating a definition portion of a source codein accordance with an advantageous embodiment;

FIG. 8 is a block diagram of an object in accordance with anadvantageous embodiment;

FIG. 9 is a diagram of an object in accordance with an advantageousembodiment;

FIG. 10 is a diagram of an action object in accordance with anadvantageous embodiment;

FIG. 11 is a diagram illustrating the application of actions inaccordance with an advantageous embodiment;

FIG. 12 is a diagram illustrating the application of actions on atimeline with a scheduler interrupt in accordance with an advantageousembodiment;

FIG. 13 is a diagram illustrating the application of events in whichtime slots overlap in accordance with an advantageous embodiment;

FIGS. 14, 15, and 16 are diagrams illustrating lasting events inaccordance with an advantageous embodiment;

FIG. 17 is a diagram illustrating an interpreter in accordance with anadvantageous embodiment;

FIG. 18 is a diagram illustrating the data flow for a lexical analyzerin accordance with an advantageous embodiment;

FIG. 19 is a diagram illustrating parsing or syntax analysis performedby a grammar parser in accordance with an advantageous embodiment;

FIG. 20 is a diagram illustrating another example of a parse tree inaccordance with an advantageous embodiment;

FIG. 21 is a diagram of a execute module in an interpreter in accordancewith an advantageous embodiment;

FIG. 22 is a flowchart of a process for generating tokens in accordancewith an advantageous embodiment;

FIG. 23 is a flowchart of a process for executing the simulation ofhuman behavior in accordance with an advantageous embodiment;

FIG. 24 is a flowchart of a process for generating sentences orproductions in accordance with an advantageous embodiment;

FIG. 25 is a flowchart of a process for executing statements forproductions in accordance with an advantageous embodiment;

FIG. 26 is a diagram illustrating a graphical user interface (GUI)processor in accordance with an advantageous embodiment;

FIG. 27 is of a diagram illustrating data flow through a graphical userinterface processor in accordance with an advantageous embodiment;

FIG. 28 is a diagram illustrating a display in accordance with anadvantageous embodiment;

FIG. 29 is a diagram illustrating manipulation of a display inaccordance with an advantageous embodiment;

FIG. 30 is a flowchart of a process for identifying changes in bitmapsin accordance with an advantageous embodiment;

FIG. 31 is a flowchart of a process for handling difference data inaccordance with an advantageous embodiment;

FIG. 32 is a diagram illustrating components for use in providing ahuman transparency paradigm in accordance with an advantageousembodiment;

FIG. 33 is a flowchart of a process for replacing a synthetic human witha live human in accordance with an advantageous embodiment;

FIG. 34 is a diagram of examples of input neurons in accordance with anadvantageous embodiment;

FIG. 35 is a diagram of examples of input ranges defined for the inputneuron left operand in accordance with an advantageous embodiment;

FIG. 36 is a diagram of a statement for input behavior in accordancewith an advantageous embodiment;

FIG. 37 is a diagram illustrating an output declaration in accordancewith an advantageous embodiment;

FIG. 38 is a diagram illustrating statements for output ranges in aneural network in accordance with an advantageous embodiment;

FIG. 39 is a diagram illustrating a statement for modifying outputbehavior in accordance with an advantageous embodiment;

FIG. 40 is a diagram illustrating statements for hidden layers inaccordance with an advantageous embodiment;

FIG. 41 is a diagram illustrating a sample neural network in accordancewith an advantageous embodiment;

FIG. 42 is example statements for training a neural network inaccordance with an advantageous embodiment;

FIG. 43 is a diagram illustrating a compute function in a neural networkin accordance with an advantageous embodiment;

FIG. 44 is a diagram illustrating an example of a neural network inaccordance with an advantageous embodiment;

FIG. 45 is a diagram illustrating the results from the operation of aneural network in accordance with an advantageous embodiment;

FIG. 46 is a diagram illustrating an example of a list in accordancewith an advantageous embodiment;

FIG. 47 is a diagram illustrating deleting a variable from the list inaccordance with an advantageous embodiment;

FIG. 48 is a diagram of code for deleting items in accordance with anadvantageous embodiment;

FIG. 49 is a diagram illustrating code to manipulate items in the listis depicted in accordance with an advantageous embodiment;

FIG. 50 is a diagram illustrating the use of the list as a queue inaccordance with an advantageous embodiment;

FIG. 51 is a diagram illustrating reading items in the list inaccordance with an advantageous embodiment;

FIG. 52 is a diagram illustrating a sort attribute in a list inaccordance with an advantageous embodiment;

FIG. 53 is an example of a fuzzy logic implementation using fueldistance and speed in accordance with an advantageous embodiment;

FIG. 54 is a diagram illustrating the solving of an equation using agenetic algorithm in accordance with an advantageous embodiment;

FIGS. 55A and 55B are a diagram illustrating code for an object insource code in accordance with an advantageous embodiment;

FIG. 56 is a diagram illustrating components used in managing a trainingprogram in accordance with an advantageous embodiment;

FIG. 57 is a flowchart of a process for managing a training program inaccordance within an advantageous embodiment; and

FIG. 58 is a flowchart of a process for managing a training program inaccordance with an advantageous embodiment.

DETAILED DESCRIPTION OF THE INVENTION

With reference now to the figures and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments may be implemented. It shouldbe appreciated that FIGS. 1-2 are only exemplary and are not intended toassert or imply any limitation with regard to the environments in whichdifferent advantageous embodiments may be implemented. Manymodifications to the depicted environments may be made.

As used herein, the phrase “at least one of”, when used with a list ofitems, means that different combinations one or more of the items may beused and only one of each item in the list is needed. For example, “atleast one of item A, item B, and item C” may include, for example,without limitation, item A or item A and item B. This example also mayinclude item A, item B, and item C, or item B and item C.

With reference now to the figures, FIG. 1 depicts a pictorialrepresentation of a network of data processing systems in which theadvantageous embodiments may be implemented. In these depicted examples,network data processing system 100 is used to implement a humanbehavioral modeling and simulation development framework. This frameworkprovides an ability to predict human behavior.

Network data processing system 100 is a network of computers and otherdevices in which advantageous embodiments may be implemented. Networkdata processing system 100 contains network 102, which is the mediumused to provide communications links between various devices andcomputers connected together within network data processing system 100.Network 102 may include connections, such as wire, wirelesscommunication links, and/or fiber optic cables.

In the depicted example, server 104 and server 106 connect to network102 along with storage unit 108. In addition, clients 110, 112, and 114connect to network 102. These clients 110, 112, and 114 may be, forexample, personal computers, workstations computers, and personaldigital assistants. In the depicted example, server 104 provides data,such as boot files, operating system images, and applications to clients110, 112, and 114. Clients 110, 112, and 114 are clients to server 104and server 106 in this example. Network data processing system 100 mayinclude additional servers, clients, and other devices not shown. Theframework for predicting human behavior in the advantageous embodimentsmay be implemented using one or more data processing systems in networkdata processing system 100.

Turning now to FIG. 2, a diagram of a data processing system is depictedin accordance with an illustrative embodiment. In this illustrativeexample, data processing system 200 includes communications fabric 202,which provides communications between processor unit 204, memory 206,persistent storage 208, communications unit 210, input/output (I/O) unit212, and display 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

Memory 206, in these examples, may be, for example, a random accessmemory or any other suitable volatile or non-volatile storage device.Persistent storage 208 may take various forms depending on theparticular implementation. For example, persistent storage 208 maycontain one or more components or devices. For example, persistentstorage 208 may be a hard drive, a flash memory, a rewritable opticaldisk, a rewritable magnetic tape, or some combination of the above. Themedia used by persistent storage 208 also may be removable. For example,a removable hard drive may be used for persistent storage 208.

Communications unit 210, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard and mouse. Further, input/output unit 212 may sendoutput to a printer. Display 214 provides a mechanism to displayinformation to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 208. These instructions may be loaded intomemory 206 for execution by processor unit 204. The processes of thedifferent embodiments may be performed by processor unit 204 usingcomputer implemented instructions, which may be located in a memory,such as memory 206. These instructions are referred to as program code,computer usable program code, or computer readable program code that maybe read and executed by a processor in processor unit 204. The programcode in the different embodiments may be embodied on different physicalor tangible computer readable media, such as memory 206 or persistentstorage 208.

Program code 216 is located in a functional form on computer readablemedia 218 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 216 and computer readable media 218 form computerprogram product 220 in these examples. In one example, computer readablemedia 218 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer readable media 218 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer readable media 218 is also referred to as computerrecordable storage media. In some instances, computer readable media 218may not be removable.

Alternatively, program code 216 may be transferred to data processingsystem 200 from computer readable media 218 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples. The computerreadable media also may take the form of non-tangible media, such ascommunications links or wireless transmissions containing the programcode.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown.

As one example, a storage device in data processing system 200 is anyhardware apparatus that may store data. Memory 206, persistent storage208 and computer readable media 218 are examples of storage devices in atangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

In the depicted example, network data processing system 100 is theInternet with network 102, representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. Of course, network data processing system 100 also maybe implemented as a number of different types of networks in addition toor in place of the Internet. These other networks include, for example,an intranet, a local area network (LAN), and a wide area network (WAN).FIG. 1 is intended as an example, and not as an architectural limitationfor different embodiments.

Turning now to FIG. 2, a diagram of a data processing system is depictedin accordance with an advantageous embodiment. Data processing system200 may be used to implement servers and clients, such as servers 104and 106 and clients 110, 112, and 114 in FIG. 1. In this illustrativeexample, data processing system 200 includes communications fabric 202,which provides communications between processor unit 204, memory 206,persistent storage 208, communications unit 210, input/output (I/O) unit212, and display 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

Memory 206, in these examples, may be, for example, a random accessmemory or any other suitable volatile or non-volatile storage device.Persistent storage 208 may take various forms depending on theparticular implementation. For example, persistent storage 208 maycontain one or more components or devices. For example, persistentstorage 208 may be a hard drive, a flash memory, a rewritable opticaldisk, a rewritable magnetic tape, or some combination of the above. Themedia used by persistent storage 208 also may be removable. For example,a removable hard drive may be used for persistent storage 208.

Communications unit 210, in these examples, provides for communicationswith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard and mouse. Further, input/output unit 212 may sendoutput to a printer. Display 214 provides a mechanism to displayinformation to a user.

Instructions for the operating system and applications or programs arelocated on persistent storage 208. These instructions may be loaded intomemory 206 for execution by processor unit 204. The processes of thedifferent embodiments may be performed by processor unit 204 usingcomputer implemented instructions, which may be located in a memory,such as memory 206. These instructions are referred to as program code,computer usable program code, or computer readable program code that maybe read and executed by a processor in processor unit 204. The programcode in the different embodiments may be embodied on different physicalor tangible computer readable media, such as memory 206 or persistentstorage 208.

Program code 216 is located in a functional form on computer readablemedia 218 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 216 and computer readable media 218 form computerprogram product 220 in these examples. In one example, computer readablemedia 218 may be in a tangible form, such as, for example, an optical ormagnetic disc that is inserted or placed into a drive or other devicethat is part of persistent storage 208 for transfer onto a storagedevice, such as a hard drive that is part of persistent storage 208. Ina tangible form, computer readable media 218 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. The tangibleform of computer readable media 218 is also referred to as computerrecordable storage media. In some instances, computer readable media 218may not be removable.

Alternatively, program code 216 may be transferred to data processingsystem 200 from computer readable media 218 through a communicationslink to communications unit 210 and/or through a connection toinput/output unit 212. The communications link and/or the connection maybe physical or wireless in the illustrative examples. The computerreadable media also may take the form of non-tangible media, such ascommunications links or wireless transmissions containing the programcode.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown.

As one example, a storage device in data processing system 200 is anyhardware apparatus that may store data. Memory 206, persistent storage208 and computer readable media 218 are examples of storage devices in atangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

As a client, data processing system 200 may take various forms. Forexample, data processing system 200 may take the form of a tabletcomputer, laptop computer, a work station, a personal computer, atelephone device, or a personal digital assistant (PDA).

The different embodiments provide a simulation environment that may beused to predict how a group of people may react individually and/or as agroup when subjected to a series of actions and/or events. In thismanner, different “what if” scenarios may be simulated with the resultsbeing used to help decision making on a final set of actions to be takenagainst the group.

The different embodiments provide a computer implemented method,apparatus, and computer usable program code for simulating humanbehavior. In one embodiment, source code is located in a storage systemin a network data processing system, such as network data processingsystem 100 as shown in FIG. 1. The source code is used for predictinghuman behavior. An interpreter executes on hardware in the network dataprocessing system. This interpreter executes the simulation with thesource code to generate a new definition and interpreted source code. Agraphical user interface processor executing on the hardware in thenetwork data processing system receives the interpreted source code andgenerates device dependent output using this interpreted source code.The device dependent output is sent to a set of devices in communicationwith the graphical user interface processor.

These devices display device dependent output and receive user input.Received user input is returned to the graphical user interfaceprocessor, which, in turn, sends the received user input to theinterpreter. The interpreter uses the received user input and the newdefinition to alter or modify the source code. In these examples, a newdefinition is information used to modify existing source code or add newinformation to existing source code. This modified source code is thenexecuted to generate new definitions and new interpreted source code. Inthis manner, the feedback loop is generated to change the source code inthese advantageous embodiments.

Turning next to FIG. 3, a diagram illustrating a simulation system isdepicted in accordance with an advantageous embodiment. System 300 is anexample of a simulation system that may be implemented in network dataprocessing system 100 in FIG. 1. In particular, system 300 may beimplemented using one or more data processing systems, such as dataprocessing system 200 in FIG. 2.

In these examples, definition 302 is processed by system 300 based onactions 304. Actions 304 are applied to definition 302 to perform thesimulation of human behavior. Actions 304 may be selected by a user ofsystem 300 in these examples. Actions 304 also may be selected from aconfiguration file or by a program or process. Definition 302 is part ofthe source code used for the simulation in these examples.

System 300 modifies definition 302 using actions 304 to generatedefinition 306. In these depicted examples, definition 306 is a newdefinition used as an output to provide results based on actions 304taken on definition 302. Additionally, definition 306 is used to modifydefinition 302, which is then used to continue performing thesimulation. This continual feedback occurs to provide system 300 anability to learn from different iterations. Additionally, the results ofprevious simulations are stored in definition 302 allowing system 300 tolearn from previous simulations.

In these examples, definition 302 is a representation of a group ofhumans and the environment in which the group of humans live. Thedescription of the environment contains the assets, in both tangible andnon-tangible form. Additionally, definition 302 also contains adescription of the different humans that may populate the group inaddition to internal relations that define actions and reactions todifferent events or input applied to the group of humans and theenvironment.

System 300 may be used as a simulation tool to predict the outcome andvarious reactions and/or impact when certain actions are taken againstthe group of humans described in definition 302. In other words, system300 may be programmed to access impacts such as, for example, economic,social, and psychological, when actions 304 are applied or taken againstdefinition 302.

In the illustrative examples, definition 302 is written using a computerlanguage. In the depicted examples, the computer language is aninterpreted language, which uses an interpreter to execute thesimulation, but does not require compiling for execution. Any languagemay be used that allows for defining various objects that are involvedin a simulation process. For example, C and C++ are examples ofinterpreted languages that may be used in these examples. In theseexamples, the definition of objects include humans and the environmentsin which the humans live. The different advantageous embodiments providea framework for system 300 as well as definition 302 and actions 304.

With reference now to FIG. 4, a diagram of a human behavioral modelingand simulation development framework is depicted in accordance with anadvantageous embodiment. Framework 400 is an example of an architecturefor system 300 in FIG. 3. In this example, framework 400 contains sourcecode 402, interpreter 404, graphical user interface (GUI) processor 406,and devices 408.

Source code 402 is a module in framework 400 that contains all of theinformation for the database. Everything known about the simulation isstored in this particular component. Source code 402 contains all of theinformation needed to run a simulation. This information includes, forexample, the definition for a group of humans and the code needed toexecute the simulation using the definition. Source code 402 alsoincludes actions that may be taken on the definition as well as codeused to present the results.

The language used for source code 402 may be modified to includefunctions and features that are specific to simulating and predictinghuman behavior. The language with these features is referred to as HumanBehavior Definition Language (HBDL). HBDL may be implemented usingcurrently available languages such as C or C++. Of course, anyinterpreted language may be used to implement HBDL in these examples.

Further, HBDL may be implemented using an entirely new language ratherthan using an existing language with modifications to provide forsimulating human behavior. In the illustrative embodiments, differentlanguages may be used to implement different components of HBDL. Inthese examples, source code 402 is a database written in HBDL and may bedistributed across different storage devices that may be located indifferent geographical locations.

Interpreter 404 collects data 410 from source code 402 to perform asimulation. In these illustrative examples, data 410 includesdefinitions of a group of humans and their environment, as well asactions that are to be applied to the humans. Further, data 410 alsoincludes statements or lines from the programming language used togenerate source code 402. These statements in data 410 are used byinterpreter 404 to perform the simulation.

The statements in data 410 may include, for example, code for anartificial intelligence program to simulate a synthetic human. Thesestatements also may include, for example, code for fuzzy logic, neuralnetworks and other processes used to perform the simulation. Inaddition, data 410 also may include processes or code for generating agraphical user interface (GUI) to present the results. In this manner,source code 402 includes both the information regarding the group ofhumans and the environment as well as the processes or code needed toexecute the simulation. Depending on the implementation, thesestatements may be in C or C++. Alternatively, the statements may be in ahigher level language that interpreter 404 translates into C or C++statements for execution.

This simulation generates graphics data 412, which is sent to GUIprocessor 406. In these examples, graphics data 412 is in a form thatdoes not require large amounts of data transmission that may slow down anetwork. In the illustrative embodiments, graphics data 412 takes theform of primitives. By transmitting primitives, rather than bitmaps orother formats that require more data to be transferred, the amount ofbandwidth used in the network is reduced. In some cases, it may benecessary to send some bitmaps in graphics data 412, but primitives areused when possible.

Instead, graphics data 412 is processed by GUI processor 406 to generatedevice data 414, which is displayed by devices 408. In these examples,device data 414 may be, for example, pixel data or bitmaps that aredisplayed by devices 408.

Devices 408 may also receive user input to generate device data 416,which is received by GUI processor 406. GUI processor 406 translatesdevice data 416 into a format that requires less use of networkresources for transmission. In these examples, user input 418 isreturned to interpreter 404. User input 418 and results from thesimulation from data 410 are used to generate modifications 420.Modifications 420 are used to overwrite or modify source code 402. Thesemodifications are used to modify definitions in source code 402.Modifications 420 are similar to definition 306 in FIG. 3, which is usedto modify definition 302 in FIG. 3. In this manner, source code 402 maybe altered to take into account the results of the simulation performedby interpreter 404 and user input received from devices 408.

Modifications 420 also may include, for example, a selection of actionsto be applied to or included in the simulation being performed byinterpreter 404. This selection of actions and modifications 420 may bereceived from user input generated at devices 408 in these examples.

Data 410 may include information, such as definitions 302 and actions304 in FIG. 3. Modifications 420 may include information, such aschanges to definition 306 in FIG. 3. These changes may be, for example,modifications to existing definitions or the addition of newdefinitions. Graphics data 412 is used to present definition 306 in FIG.3 in these examples.

The illustration of the different modules in framework 400 is not meantto imply architectural limitations to the manner in which these modulesmay be implemented. For example, the different modules may includedifferent sub-modules or processes to implement the different featuresin framework 400. Also, a particular module may be implemented on asingle data processing system or spread across multiple data processingsystems.

With the modularity of framework 400, the different modules may bedistributed to different locations across a network to maximize the useof hardware resources. This modularity in framework 400 also allows forthe centralization of some functionality while allowing otherfunctionality to be migrated or distributed to remote locations in anetwork data processing system. For example, placing graphics processingand device dependent data in a centralized environment and then sendingthis information over a network to remote device provides fewadvantages. Graphics data, in general, is large in size and may slowdown a network. As a result, centralization of this type of informationand processing introduces problems with respect to latency, datatransmission and data synchronization. Framework 400 is designed suchthat implementations of framework 400 may avoid these problems.

With reference next to FIG. 5, a diagram illustrating a distribution ofmodules in a framework is depicted in accordance with an advantageousembodiment. In this example, the different modules illustrated in system500 are from framework 400 in FIG. 4. As can be seen in this depictedexample, system 500 contains internet 502, local area network (LAN) 504,wide area network (WAN) 506 and local area network (LAN) 508. Thesedifferent networks are an example of components in network 102 in FIG.1.

As depicted, source code 510 is found in storage device 512, 514, and516. Storage device 512 is connected to local area network 504; storagedevice 514 is connected to wide area network 506; storage device 516 isconnected to local area network 508. The distribution of source code 510from different devices in different networks is an example of one mannerin which source code 510 may be stored.

Source code 510 also may be stored on a single storage system on aparticular network rather than across different locations. Some storagedevices storing source code 510 may be backup devices that storeduplicate copies of source code 510. With this implementation, it ispossible to migrate portions of the system of other locations toleverage advantages that may be found in those locations, rather thanlimiting the implementation to a particular place.

Interpreter 518 is located on data processing system 520 in thisexample. Data processing system 520 may be implemented using a dataprocessing system, such as data processing system 200 in FIG. 2. Dataprocessing system 520 is connected to local area network 508.Interpreter 518 collects data from source code 510 in the differentstorage devices across the different networks to perform a simulation.

The results of the simulation are sent to GUI processor 522, which isalso located on data processing system 520. GUI processor 522 generatesdevice data for display on devices 523. Additionally, interpreter 518may send graphics data to GUI processor 524 executing on data processingsystem 526 and GUI processor 528 executing on data processing 530. GUIprocessor 524 generates device data for display on devices 530, whileGUI processor 528 generates device data for display on devices 532. GUIprocessors 522, 524, and 528 are located close to devices 523, 530, and532, respectively.

In this manner, the data generated by these processors does not requirethe use of large amount of network resources. In these examples, the GUIprocessor module described in framework 400 in FIG. 4 is duplicated inseveral different locations within system 500 to minimize the use oftransmitting graphics data over a network to remote device in a mannerthat may slow down or use large amounts of network resources.

With reference now to FIG. 6, a diagram illustrating a source modulecode is depicted in accordance with an advantageous embodiment. In thisexample, source code 600 is a more detailed illustration of source code402 in FIG. 4.

Source code 600 contains definition 602, actions 604, and graphical userinterface (GUI) language 606. Definition 602 and actions 604 aredirected towards the simulation of a group of humans in an environment.GUI language 606 is employed to present results and receive user inputfrom the end user of the simulation. With GUI language 606, source code600 controls the appearance of the presentation of results on thedevices. This appearance of results is controlled using GUI language 606in these examples.

Further, source code 600 is adaptive and open in these examples. Sourcecode 600 includes both the information for the simulation and the actuallanguage used to execute or perform the simulation. Source code 600takes away decision making from a traditional application that reads andinterprets a database. In contrast, source code 600 is a database thatcontains both the information and the application in which theinformation and application may be altered based on results generatedthrough performing simulations.

Data 608 represents a flow of data to an interpreter, such asinterpreter 404 in FIG. 4. Modifications 610 presents changes to sourcecode 600 being received from the interpreter. Data 608 includesinformation from definition 602, actions 604, and GUI language 606 tothe interpreter for use in performing the simulation. In these examples,source code 600 is a free format database.

In these illustrative examples, source code 600 is written in HBDL. As afree format database, source code 600 does not require success ofseparators between different components. A program within source code600 may be written using a single line. Source code 600 also containsloops, case statements, conditional jumps, and other similar statementsto alter the execution of the simulation.

Further, source code 600 contains objects, which store portions of code.As a result, objects may be called over and over without the need forrepetitions. Further, within source code 600, objects may be indexed anddefined to contain default parameters. In this manner, the creation ofintelligent objects is enabled within source code 600.

Also, different types of variables may be defined within source code600. These types of variables may be directed toward particular tasks.For example, in addition to conventional numerical types, source code600 may include types, such as humans, persons, family, action,timeline, date, and others. Additionally, source code 600 provides atimeline based execution model for performing simulations. Artificialintelligence components may be provided within source code 600 alongwith function commands to support these components.

Within source code 600, definition 602 describes a group of humans andthe environment in which the groups of humans live. Actions 604represent influences that are applied to definition 602 during thesimulation. In these examples, actions 604 are portions or snippets ofcode referred to as events that are put into a timeline.

In these examples, humans taking part in the simulation as well ashumans using the simulation are handled by source code 600. The humanstaking part in the simulation may be real or synthetic humans.Definition 602, actions 604, and GUI language 606 include functioningcode as well as parameters. The functioning code and parameters alongwith other information are output as data 608 for interpretation by aninterpreter.

By moving this information into source code 600, source code 600 is ableto take control of the simulation. In this manner, the simulation is nolonger application specific, like those written currently used stylesand languages. For example, once a particular item is defined indefinition 602, this item may be used with a predefined set of actionsin actions 604. For example, a human of type X may be defined indefinition 602 used along with actions run in actions 604. As a result,an infinite number of simulations may be created and run withoutrecoding human X and run for each simulation.

Further, having GUI language 606 located within source code 600 meansthat definition 602 and actions 604 may control the display presented tousers. In this manner, source code 600 is essentially a database that isin charge of what users of the simulation see. This feature alsosupports the reusability of definitions and knowledge to allow aninfinite number of simulations to be created without having to customwrite a simulation for application or program for each simulation.

Current systems employ data that is static in a single simulation thatis coded from scratch. At best, the code that simulates a single objectis kept in a library, but the action for each object is unique to eachsimulation. The action is typically written in a separate program. As aresult, currently used techniques require substantial coding for eachparticular type of simulation. Further, with current practices, agraphical user interface is typically reused and in control of theapplication.

As a result, these interfaces do not change a particular simulationwithout having to recode or rewrite the application. In this manner,with the source code design in the advantageous embodiments, greaterflexibility is presented to display results and receive user input incontrast to currently used techniques for simulations.

GUI language 606 provides code that is selectively sent by interpreterthrough a graphical user interface processor, such as GUI processor 406in FIG. 4. This code provides the displays or visuals to the end user aswell as input and output controls that are provided at various displays.GUI language 606 controls what every end user sees on their screens andthe way every user interacts with the system. In these illustrativeexamples, GUI language 606 is a subset of HBDL. Depending on theparticular implementation, GUI language 606 may be implemented using adifferent language to provide the different advantageous features. Inthis manner, the different advantageous embodiments show the control tosource code 600.

An advantage of having the input and output controlled by GUI language606 within source code 600 is that the user interface provided at theend devices may be controlled by the simulation. A simulation ofteninvolves users having different backgrounds. An ability to customize thevarious user interfaces helps these users to quickly understand thesystem. Thus, the learning curve for executing simulations is reduced.Further, only the most relevant information is presented to differentusers, which enhances the relevance of the simulation.

For example, different users may require different user interfaces for aparticular simulation. Some users may be provided a user interface toselect particular actions from actions 604 to apply to definition 602.Other users may be able to take the place of a synthetic human definedin definition 602. This type of user is provided a different userinterface from the user that selects actions.

Further, different simulations being executed also require differenttypes of interfaces. This type of architecture also provides an abilityto dynamically add or simplify user interfaces. In this manner, userinterfaces may be provided in an implementation dependent manner.

Additionally, different simulations may impose new parameters and newenvironments. These new and changing situations mean that different datasets are to be analyzed. These situations also may involve the need fordifferent users or experts to be involved. This type of ever changingnature of the task at hand imposes a need for a versatile adaptiveenvironment as provided through source code 600. Source code 600provides this paradigm within definition 602, actions 604, and GUIlanguage 606.

In these illustrative examples, the GUI processor is under the controlof source code 600 and not a static application as currently occurs withpresently used simulation systems. GUI language 606 provides a layer ofabstraction for the hardware. The content of GUI language 606 ensuresthat the simulation process occurs in a predictable fashion and thatappropriate information is delivered and received from a variety ofusers and devices. GUI language 606 contains all of the code needed torun on any hardware on which an interpreter and a GUI processor areimplemented. In this manner, only lower layer portions that wrap aroundhardware need to be rewritten when hardware changes occur.

In these illustrative examples, GUI language 606 provides a variety ofconstructs to build necessary elements of a user interface duringruntime. These elements include, for example, mouse tracking movement,mouse clicking movement, an analog joy stick, menus, windows, dialogboxes, check boxes, radio buttons, list boxes, and forms. With these andother elements, implementing a graphical user interface is made easier.Further, the output generated in building a graphical user interfaceusing GUI language 606 may be saved within GUI language 606 for lateruses.

GUI language 606 provides a number of different features to allow sourcecode 600 to present and receive input with respect to a simulation of agroup of people and the environment in which they are living in. GUIlanguage 606 provides a set of three dimensional primitives. These threedimensional primitives support features such as a set of commands tocontrol a hypothetical camera and view port. Mathematical functions,including vector and matrix operations, are also included along with anability to import and export graphics file formats of different types.

The features provided within GUI language 606 also include creatingthree dimensional objects that embed more than just three dimensionaldata. For example, these three dimensional objects may include otherinformation, such as, for example, price, weight, color, value, or rulesregarding the three dimensional object. Of course, any kind ofinformation may be imbedded or associated with these three dimensionalobjects.

Further, GUI language 606 also includes a three dimensional model andstack, and a set of commands to manage this model and stack. The threedimensional model and stack allows for a creation of complextransformations to be applied to different three dimension entities. Inthis manner, different worlds may be created in which objects aretemporarily affected.

GUI language 606 provides for an easy creation and maintenance of largethree dimensional databases. These databases may be found withindefinition 602. The databases may be used to represent any threedimensional object regardless of the size, complexity, or nature of theobject.

Moreover, GUI language 606 provides a graphical user interface buildinglanguage. This language allows source code 600 to control the look andfeel of every end user device. GUI language 606 may include twodimensional primitives, such as points, lines, curves, and surfaces.Further, a set of two dimensional control objects also are present.These two dimensional control objects include, for example, windows,dialog boxes, requesters, check boxes, radio buttons, and menus.

With reference next to FIG. 7, a diagram illustrating a definitionportion of a source code is depicted in accordance with an advantageousembodiment. Definition 700 is a more detailed illustration of definition602 in FIG. 6. Definition 700 includes assets 702, humans 704, andinternal relations 706.

Assets 702 includes both tangible assets 708 and non-tangible assets 710in the environment in which the humans are present. Tangible assets 708include both live and inanimate objects. Live objects may include, forexample, livestock, birds, bacteria, and plants. Inanimate objects mayinclude, for example, a house, a mountain, a lake, a car, a table, apen, an aircraft, or a gun.

Non-tangible assets 710 may include, for example, rules, laws, andregulations for a group of humans being simulated. Non-tangible assets710 may also include information used by the interpreter to handle theassets. This information also includes general code, libraries, androutines.

More specifically, this type of asset includes, for example, a mathlibrary, a graphics library, a two dimensional primitives library, athree dimensional primitives library, a model and stack managementlibrary, an artificial intelligence library, an input/output library, anencryption library, a networking library, a system calls library, and atime management library. In other words, non-tangible assets 710 mayinclude any information needed to execute the simulation.

Humans 704 describe the various human characters that are present in agroup of humans. Humans 704 may include information that details variousfamily trees and relationships among the people in the group of humans.Further, humans 704 contain information needed to create psychologicalprofiles for the various individuals.

Internal relations 706 contain actions and reactions that are used bythe artificial intelligence in definition 700. These actions andreactions may be triggered in various manners. For example, the triggermay be random, alarm based, a state machine, or as a reaction to a setof events that are applied to definition 700.

Different objects within assets 702 may rely upon non-tangible assets710 to perform necessary functions and computations. The general code,libraries, and routines are the code needed to support differentprogramming tasks. These different components may be sent as data to aninterpreter for use in executing a simulation. The three dimensionalobjects are all objects that make up the different world, including liveand inanimate objects.

With reference now to FIG. 8, a block diagram of an object is depictedin accordance with an advantageous embodiment. In this example, object800 is an example of one illustrative implementation of an object withindefinition 602 in FIG. 6. In this illustrative example, object 800includes artificial intelligence 802, characteristics 804, and internalrelations 806.

Artificial intelligence 802 contains code used to simulate a particularobject. In these examples, object 800 is a living object, such as ahuman being, a plant, or an animal. Artificial intelligence 802 containsthe code necessary to simulate the actions and reactions of the selectedobject.

Characteristics 804 include an identification of characteristics for theparticular object. For example, if object 802 is a human,characteristics 804 may include, for example, height, weight, skincolor, hair color, eye color, body build, and any other suitablecharacteristic of a person. Characteristics 804 may include otherphysical characteristics, such as, for example, how fast the person canrun, the agility of the person, and the stamina of the person.

Non-physical characteristics in characteristics 804 may include, suchas, for example, without limitation, patience, compassion, emotions,intelligence, and interpersonal relationships. Characteristics 804 areused by artificial intelligence 802 to simulate actions and reactions ofobject 800. In particular, characteristics 804 are used to simulatehuman behavior in the depicted examples.

The complexity of artificial intelligence 802 and the number ofcharacteristics within characteristics 804 will vary depending on theparticular implementation. The complexity of these components increaseas the desired ability to make the simulation indistinguishable fromreal objects increases.

Internal relations 806 contains actions and reactions that may be usedby artificial intelligence 802 to trigger events. These events include,for example, actions taken by object 800. These actions may be initiatedby object 800, or the actions may be ones that occur in response toactions taken on object 800. These actions taken on by object 800 may beactions directed towards object 800 or may be ones that are perceived byobject 800 based on the environment in which 800 is in during asimulation.

Turning now to FIG. 9, a diagram of an object is depicted in accordancewith an advantageous embodiment. In this example, object 900 is anexample of an inanimate object that may be simulated with definition 602in FIG. 6. Object 900 may be, for example, a car, a pen, an aircraft, amountain, or a lake.

Object 900, in this example, includes model 902 and characteristics 904.Model 902 includes code that is used to simulate the particular object.Model 902 includes code to simulate the functions of the particularobject. For example, if object 900 is a car, various actions may beperformed on the car that generates certain results. For example, theengine may be turned on and the wheels may turn.

Model 902 may be, for example, a mathematical model. For example, set offinite state machines may be used to model the functions and operationof a car. Other functions and processes may be included in model 902,such as those to simulate aging of the object being modeled through useand exposure to environments over time.

Characteristics 904 identifies various characteristics of the car, suchas, for example, tire size, engine size, paint color, type of radio, andamount of interior space. Further, characteristics 904 also may includeother information about features of the car for object 900. For example,the amount of tread on a tire may be identified for the particular tiretype within characteristics 904.

Model 902 is used to simulate what the car does in response to variousactions taken on by object 900, such as a user driving the car, wear andtear that occurs on the tires identified in characteristics 904. Thiswear and tear is recorded within characteristics 904. The wear and tearmay be part of an algorithm within model 902. Further, environmentalexposure such as sun and hail may be taken into account by model 902 topresent an aged look for model 902 in the car example. The differentactions performed by object 800 in FIG. 8 and object 900 in FIG. 9 maybe performed on these objects and may be defined within actions 604 inFIG. 6.

With reference now to FIG. 10, a diagram of an action object is depictedin accordance with an advantageous embodiment. In this example, actionobject 1000 is an example of an action that may be found within actions604 in FIG. 6.

Action object 1000 includes action 1002, object 1004, user permissions1006, and graphical user interface (GUI) 1008. Action 1002 may be, forexample, an action that may be performed, such as talk, strike, move,sit, grasp, speak, or look. Object 1004 is an identification of theobject in which the action may be taken. User permissions 1006determines whether the particular user may perform action 1002 on object1004. Graphical user interface 1008 identifies the type of userinterface that is presented to a particular user.

Object 1004 may be an inanimate object or a live object. Userpermissions 1006 are used to determine whether certain users are able toperform selective actions on an object. In some cases, it is undesirablefor a particular user to perform an action on an object. Graphical userinterface 1008 identifies the manner in which the action on the objectis presented to the user as well as how user interactions with theobject may occur.

The illustrations of objects in FIGS. 8, 9, and 10 are presented forpurposes illustrating one manner in which source code 600 in FIG. 6 maybe implemented using currently available programming languages andmethodologies. These examples, however, are not meant to implylimitations on the manner in which source code 600 in FIG. 6 may beimplemented.

Turning now to FIG. 11, a diagram illustrating the application ofactions is depicted in accordance with an advantageous embodiment. Inthese examples, timeline 1100 illustrates influences that a definition,such as definition 602 in FIG. 6, is subjected to during a simulation.In these illustrative examples, actions such as actions 604 in FIG. 6are referred to as events on timeline 1100. These actions are snippetsor portions of code. In particular, the actions include events 1102,events 1104, events 1106, and events 1108. In this example, events 1102are applied at time slot 1110. Events 1104 occur during time slot 1112and events 1106 are applied during time slot 1114. Events 1108 occurduring time slot 1116. These events may be procedural in nature or eventdriven. In other words, the events may be applied in response to variousmessages emitted or generated by the interpreter.

In these examples, a master interrupt issued by the execution oftimeline 1100 interrupts these events during mid-execution, ifnecessary, and the simulation passes immediately to the next time slotof the simulation. Unlike currently used program languages, in which theexecution of source code is either procedural or event driven, theactions in the source code follow a time-based execution model in theadvantageous embodiments. In these examples, the time slots may havevarious granularities. For example, each time slot may represent a week,a day, an hour, a minute, or some other period of time.

In the depicted embodiments, timeline 1100 runs under the supervision ofa scheduler, which is described in more detail below. The scheduler runsevents 1102, 1104, 1106, and 1108, which are associated or attached totimeline 1100. The scheduler has full control over these events and mayinterrupt them as necessary. Further, the scheduler runs a memorymanager and a memory recovery facility. In this manner, all memoryallocated for an interrupted task may be made available to upcomingevents.

Turning now to FIG. 12, a diagram illustrating the application ofactions on a timeline with a scheduler interrupt is depicted inaccordance with an advantageous embodiment. In this example, timeline1200 includes events 1202 and events 1204. Events 1202 begins executingduring time slot 1206 on timeline 1200. In this example, events 1202includes input 1208, decisions 1210, and process 1212. Events 1202begins execution during time slot 1206. When time slot 1206 ends, thescheduler interrupts the execution of events 1202 at point 1214.Execution then is transferred to events 1204 for time slot 1216, whichbegins after time slot 1206. In this example, no overlap is presentbetween time slot 1206 and time slot 1216.

Turning next to FIG. 13, a diagram illustrating the application ofevents in which time slots overlap is depicted in accordance with anadvantageous embodiment. In this example, the scheduler executestimeline 1300, which has events 1302, 1304, 1306, and 1308 attached tovarious time slots. Events 1302 are attached to time slot 1310. Events1304 are attached or associated with time slot 1312. Events 1306 and1308 are attached to time slots 1314 and 1316, respectively.

In this illustrative example, the different time slots may overlap witheach other. In other words, one time slot may last for a longer periodof time than another time slot. As illustrated, time slot 1310 and timeslot 1312 overlap with each other. As a result, events 1302 and events1304 may run concurrently during a certain period of time, in which timeslots 1310 and time slots 1312 overlap. In this particular example,events 1302 are provided with more time to execute.

The overlapping of time slots does not mean that events are mergedduring the moment of time in which the overlap occurs. In theseillustrative examples, if for any reason an interrupt of events 1302 fortime slot 1310 occur, control passes to time slot 1312 if this interruptoccurred before the start of or during the execution of time slot 1312.If, however, the interrupt occurred during time slot 1310, but after theend of time slot 1312, control is passed to the execution of events 1306in time slot 1314.

With reference now to FIGS. 14, 15, and 16, diagrams illustratinglasting events are depicted in accordance with an advantageousembodiment. As illustrated, timeline 1400 contains events 1402, lastingevent 1404, and events 1406, which are attached to or assigned to timeslot 1408. Events 1410 are associated with time slot 1412 on timeline1400. Events 1414 are attached to time slot 1416, while events 1418 areattached to time slot 1420. Events may be made uninterruptible or givenextensions. This type of event may be present when waiting for an inputcoming from an end user or from some other event that has not yetterminated but is needed. In these examples, this type of event is alasting event, such as lasting event 1404. Lasting event 1404 may becarried over from one time slot to another time slot until the eventfully occurs.

As illustrated in FIG. 15, lasting event 1404 becomes attached to timeslot 1412. In FIG. 16, lasting event 1404 is again extended or moved totime slot 1416. Lasting event 1404 is completed during this particulartime slot in these examples.

Turning now to FIG. 17, a diagram illustrating an interpreter isdepicted in accordance with an advantageous embodiment. Interpreter 1700is a more detailed illustration of interpreter 404 in FIG. 4.Interpreter 1700 is a program that transforms a source code written inone language into a target code written in another language. Interpreter1700 also executes the target code as the interpreter proceeds inprocessing the source code. This target language may be written inanother high-level language or in the language used by the particulardata processing system or processor.

For any program to be correctly interpreted and executed, the sourcecode is structured according to constructs defined by the language. Inparticular, these constructs are syntactical constructs. The completeset of constructs forms the grammar of the language for the source code.Any code that is not structured according to these constructs or isgrammatically incorrect is discarded by interpreter 1700.

Interpreter 1700 includes communication modules 1702 and languageinterpreter 1704. Additionally, interpreter 1700 includesencryption/decryption modules 1706, which provide for secure sending andreceiving of information between interpreter 1700 and a GUI processor,such as GUI processor 406 in FIG. 4.

In these illustrative examples, language interpreter 1704 receives HBDL1708. HBDL 1708 is an example of data, such as data 410 in FIG. 4received from a source code module, such as source code 402 in FIG. 4.HBDL 1708 is interpreted by language interpreter 1704 to execute asimulation. The results are interpreted HBDL (IHBDL) 1710, which aresent to encryption/decryption module 1706 for encryption. Afterencryption, the encryption results are sent as encrypted interpretedHBDL (EIHBDL) 1712 to the GUI processor, such as GUI processor 406 inFIG. 4. EIHBDL 1712 is an example of graphics data 412 in FIG. 4. Userinput is received as encrypted HBDL (EHBDL) 1714 as collected by a GUIprocessor from a set of devices. EHBDL 1714 is an example of user input418 in FIG. 4. This encrypted information is decrypted and sent tocommunication modules 1702 as HBDL 1716.

HBDL 1716 is an example of user input that is used to modify the sourcecode. This modification may be, for example, changing definitions withinthe source code or to select actions to be applied to the definitions.Additionally, the output of language interpreter 1704 is sent tocommunications module 1702 as HBDL 1718 for use in modifying the sourcecode. HBDL 1716 and HBDL 1718 are used by communication modules 1702 toform HBDL 1720, which is used to modify the source code. HBDL 1720 is anexample of a format for modifications 420 in FIG. 4, which are used tomodify the source code. As can be seen, HBDL 1718 provides a feedbackfor the output generated by language interpreter 1704 to modify thesource code.

More specifically, communication modules 1702 includes dispatcher module1722, input module 1724, and registration module 1726. Languageinterpreter 1704 includes lexical analyzer 1728, grammar parser 1730,and execute modules 1732.

Language interpreter 1704 contains modules that deal with lexicalanalysis, grammar parsing, and decision making. When data is received asHBDL 1708, lexical analyzer 1728 dissects the data in HBDL 1708 intoindividual tokens or words in the source code language. In theseillustrative examples, the source code is written in HBDL. In otherwords, lexical analyzer 1728 identifies the different tokens orcomponents in HBDL 1708. These tokens are sent to grammar parser 1730which groups the tokens into meaningful sentences or statements for HBDL1708. Once a sentence or statement in HBDL 1708 is constructed, grammarparser 1730 sends this statement to execute module 1732. Action based onthis statement is then accordingly taken.

Execute module 1732 includes a number of different sub-modules forperforming simulations. In these examples, execute module 1732 generatesinterpreted HBDL (IHBDL) 1710 and HBDL 1718. HBDL 1710 takes the form ofgraphics data, such as graphics primitives. HBDL 1718 is a modified ornew definition that is used to modify the source code. HBDL 1718 isreturned to input module 1724 for use in modifying or rewriting thesource code. Input module 1724 passes the new definition in HBDL 1718 todispatcher module 1722 which dispatches HBDL 1720 to be written into thesource code.

In these illustrative embodiments, grammar parser 1730 launches lexicalanalyzer 1728 and execute module 1732. Grammar parser 1730 requeststokens from lexical analyzer 1728. Lexical analyzer 1728 receivescharacters from HBDL 1708 to generate tokens. Each time a token isgenerated, lexical analyzer 1728 sends the token to grammar parser 1730.Grammar parser 1730 generates one or more parse trees using the token.When a parse tree is completed, grammar parser 1730 requests an actionto be executed by execute module 1732 based on the completed parse tree.

In these illustrative examples, each parse tree represents a production.A production has a set of one or more actions that are fired or executedeach time a stream of tokens matches a definition for a production. Inresponse to the request from grammar parser 1730, execute module 1732performs a semantic analysis to determine whether any semantic errorsare found in the instructions for the actions. If an error occurs, theerror is reported. Otherwise, the instructions for the set of actionsare executed. A recursive return to non-terminal callers is made withactions being fired for the assigned completed productions along theway.

In these examples, execute module 1732 determines whether theinstructions generated in the parse trees created by grammar parser 1730are semantically correct. If semantic errors occur, execution module1732 generates an error and ignores the instruction in these examples.In some cases, however, the errors may be corrected if enoughinformation is present for execute module 1732 to make corrections.

When user input is received as EHBDL 1714, encryption/decryption modules1706 decrypt the information to form HBDL 1716. HBDL 1716 is the userinput in an un-encrypted form of HBDL, which is received by registrationmodules 1702. Registration module 1726 registers and validates each ofthe users that return user input. This registration module ensures thatonly authorized or registered users are allowed to return input into thesystem. For example, registration module 1726 may validate the passwordfor a particular user.

Once a user is validated, the user input in HBDL 1716 is passed on toinput module 1724. Input module 1724 acts as a focal point to centralizeall forms of input and send the input with specific instructions todispatcher module 1722. Input module 1724 may add instructions needed bydispatcher module 1722 to process the input. The input defines what tochange in the source code in these examples.

The specific instructions include instructions, such as, instructionsregarding what portions of the source code can be modified by aparticular user in these examples. For example, if the user inputmodifies a definition, the instruction tells what portion of the sourcecode is to be modified. The portion of the source code to be modified bythe input may be identified using the identification of the usergenerating the input and the input itself.

Then, dispatcher module 1722 ensures that the appropriate section ofsource code is rewritten when HBDL 1720 is sent back to the source code.Dispatcher module 1722 determines whether to write to the source codeusing a policy with the identification of the user, the input, and theportion of the source code to be written. The policy is a set of rulesused to determine whether writes should be made to the source code inresponse to the input. This policy provides a redundancy to preventcases in which an unauthorized user may get past registration module1726. For example, an unauthorized user may spoof a real user and submitinput. The policy may identify the input as changes that are not to bemade by the real user or not characteristic of the input made by thereal user. In this case, the input is rejected by dispatcher module1722.

In these examples, each user has their own set of actions that the usermay add to or modify. As a result, users can only modify the actionsegment of the source code. The output, HBDL 1718, from execute module1732, may be used to rewrite the definitions as well as actions. Inthese examples, language interpreter 1704 is also considered anotheruser in the system. The interpreter, however, is a permanentlyauthenticated user. Dispatcher module 1722 sees language interpreter1704 as such to rewrite definitions using HBDL 1718.

As a result, at any given point of the simulation, a real human end usermay replace any defined human in the database. Through this data flow,interpreter 1700 may rewrite or alter the source code. As time passesand simulations are run, definitions are constantly being generated forrewriting the source code.

Input module 1724 provides a connection between the “virtual world”being executed in the simulation and the “real world” as being receivedthrough user input by users at devices in communication with the system.Dispatcher module 1722 provides a mechanism to write new definitions tomodify the source code.

Turning next to FIG. 18, a diagram illustrating the data flow for alexical analyzer is depicted in accordance with an advantageousembodiment. As illustrated, lexical analyzer 1800 receives source 1802and processes source 1802 to generate tokens 1804. Lexical analyzer 1800is an example of lexical analyzer 1728 in FIG. 17. Lexical analyzer 1800reads the content from source 1802 character by character and groups theincoming characters from source 1802 into basic units referred to astokens, such as tokens 1804.

The grouping of characters in source 1802 into tokens 1804 is performedin these illustrative examples using a set of token descriptions inregular expressions 1806. Regular expressions 1806 contains thedescriptions needed to group characters in source 1802 into tokens 1804.In these examples, regular expressions 1806 may be implemented usingscripts. These scripts use symbols of a language that describescharacter patterns for use in grouping characters into tokens.

Each regular expression defined in regular expressions 1806 is assigneda symbol. This symbol typically is a number. The token generated intokens 1804 by lexical analyzer 1800 is identified using the symbol forthe particular regular expression in regular expressions 1806.

In addition to regular expressions 1806, lexical analyzer 1800 also usesreserved words 1808. Words within reserved words 1808 also are assigneda symbol when a token is identified within source 1802. A reserved wordis a word that has special grammatical meaning to a language and cannotbe used as an identifier in that language.

Turning now to FIG. 19, a diagram illustrating parsing or syntaxanalysis performed by a grammar parser is depicted in accordance with anadvantageous embodiment. The parsing illustrated in FIG. 19 may beperformed by grammar parser 1730 in FIG. 17. Tree 1900 is an example ofa parse tree that may be used by a grammar parser to group tokens. Inthis example, parsing of statement 1902 is illustrated. Statement 1902is var1=20. This statement is used to manage or define actions to becarried out by the interpreter.

In particular, the grammar parser identifies relationships betweentokens produced by the lexical analyzer based on a set of syntacticconstructs also called productions. Each production represents a logicalunit and is typically defined in terms of tokens in other logical units.Most languages define two broad types of logical units. These logicalunits are statements and expressions in these examples. Expressions areusually syntactic language constructs that provide values. Statementsare syntactic constructs that change the state of variables, control theflow of the program, or perform other operations supported by thelanguage.

The grammar parser groups the stream of tokens into logical units andinstructs the execute module to execute actions based on the logicalunits. In this example, statement 1902 contains a stream of tokensproduced by the lexical analyzer. These tokens are varname 1904, EQ1906, expression 1908, and NL 1910. Expression 1908 contains an integer,INT 1912. The value for varname 1904 is var1; the value for EQ 1906 is=; the value for INT 1912 is 20; and the value for NL 1910 is \n. As canbe seen, given a stream of tokens, the grammar parser regenerates thegrammar based on the sequence of tokens in the stream.

Turning now to FIG. 20, a diagram illustrating another example of aparse tree is depicted in accordance with an advantageous embodiment. Inthis example, parse tree 2000 is generated from statement 2002.Statement 2002, in this example, is output=var1+var2*var3.

In this example, tokens for statement 2002 include varname 2004, EQ2006, varname 2008, plus 2010, varname 2012, MUL 2014, varname 2016, andNL 2018. Varname 2004 has a value output; EQ 2006 has a value =; varname2008 has a value var1; plus 2010 has a value +; varname 2012 has a valuevar2; MUL 2014 has a value *; varname 2016 has a value var3; and NL 2018has a value \n. The expression on the other side of the = sign forstatement 2002 is defined by tokens varname 2008, plus 2010, varname2012, MUL 2014, and varname 2016.

The identification of these expressions and their usage in the languageis indicated within parse tree 2000 through the location of these tokenswith respect to identifying nodes. For example, expression 2020indicates that expressions 2022 and 2026 are operated upon usingoperator 2024. In this case, operator 2024 is plus 2010. Expression 2020also includes expression 2026, which identifies expressions 2028 and2030 as being operated upon using operator 2032. In this example,expression 2022 contains varname 2008, while expression 2026 includesthe results of applying operator 2032 to expressions 2028 and 2030.

Turning now to FIG. 21, a diagram of a execute module in an interpreteris depicted in accordance with an advantageous embodiment. In thisexample, execute module 2100 is a more detailed illustration of executemodule 1732 in FIG. 17. As illustrated, execute module 2100 includesmaster timeline control module 2102, math module 2104, physics module2106, artificial intelligence (AI) module 2108, report generator 2110,and graphics module 2112.

Master timeline control module 2102 is a scheduler that is used to applyevents to the definitions over time within execute module 2100. Mathmodule 2104 and physics module 2106 provide for calculations needed todetermine effects of actions on different objects. Artificialintelligence module 2108 is a component used to run source code fordifferent artificial intelligence components to aid in the simulation ofhuman behavior as events are applied to definitions by master timelinecontrol module 2102.

Graphics module 2112 generates graphics data to send to the GUIprocessor for presentation at the end devices. Report generator 2110generates two types of outputs in these examples. One type of output isa new definition that is used to modify the source code. This outputgenerated is, for example, HBDL 1718 in FIG. 17. The other type ofoutput generated by report generator 2110 is the graphics data, whichalso is formatted in HBDL in these examples. This output is, forexample, IHBDL 1710 in FIG. 17.

Graphics module 2112 includes a number of different types of processesused to generate output representing results of a simulation to a user.These types of processes include, two dimensional graphics pipelines,two dimensional graphics primitives, three dimensional graphicspipelines, three dimensional graphics primitives, two dimensional andthree dimensional model and stacks, a display list generator, and twodimensional and three dimensional rendering engines. These, as well asother types of graphics processes, may be present in graphics module2112 for use in generating output for presentation to a user.

Turning now to FIG. 22, a flowchart of a process for generating tokensis depicted in accordance with an advantageous embodiment. The processillustrated in FIG. 22 may be implemented in a software component, suchas lexical analyzer 1728 in FIG. 17.

The process begins by receiving the next character from the source(operation 2200). In these examples, the source of characters is HBDL1708 in FIG. 17. The character is placed into a queue (operation 2202).

Next, a determination is made as to whether a match is present betweenthe string in the queue and regular expression or a reserved word(operation 2204). If a match is present, a token is created using thestring in the queue (operation 2206). The queue is then cleared(operation 2208).

Thereafter, a determination is made as to whether an end of file hasbeen reached in the source (operation 2210). If an end of file has beenreached, the process terminates. Otherwise, the process returns tooperation 2200 to obtain the next character. If a string match does notoccur in operation 2204, the process proceeds to operation 2210 asdescribed above.

Turning now to FIG. 23, a flowchart of a process for executing thesimulation of human behavior is depicted in accordance with anadvantageous embodiment. The process illustrated is FIG. 23, may beimplemented in a framework, such as framework 400 in FIG. 4. Inparticular, this simulation may be executed using source code, such assource code 600 in FIG. 6.

The process begins by populating a virtual environment with a set ofhumans defined in a definition within a source code (operation 2300). Inthese examples, the definition is a definition, such as definition 602in FIG. 6. The process executes a set of actions on the set of humans inthe virtual environment using actions within the source code to form aresult that simulates the human behavior (operation 2302). In theseexamples, the set of actions may be taken from actions, such as actions604 in FIG. 6.

Thereafter, an output is generated from the result using graphicalinterface language in the source code to form a formatted output(operation 2304). In these examples, the graphical user interfacelanguage may be GUI language 606 in FIG. 6. Thereafter, the formattedoutput is presented on a set of devices in the network data processingsystem as the simulation occurs (operation 2306), with the processterminating thereafter.

In this manner, the different advantageous embodiments simulate humanbehavior through a source code that may change as the simulation occurs.Further, the graphical user interface language allows for the manner inresults are presented to a user to change and be controlled by thesimulation itself.

Turning now to FIG. 24, a flowchart of a process for generatingsentences or productions is depicted in accordance with an advantageousembodiment. The process illustrated in FIG. 19 may be implemented in asoftware component, such as grammar parser 1730 in FIG. 17.

The process begins by obtaining the next token for processing (operation2400). In these examples, the token is received from a lexical parser,such as lexical parser 1728 in FIG. 17. A determination is made as towhether an end of file has been encountered with respect to the token(operation 2402). If an end of file has not been encountered, adetermination is made as to whether the token fits into a parse tree(operation 2404). If the token does not fit into a parse tree, an erroris generated (operation 2406) with the processing then returning tooperation 2400.

Otherwise, a determination is made as to whether the token completes aparse tree (operation 2408). If the token completes the parse tree,execution of instructions for a production corresponding to thecompleted parse tree occurs (operation 2410). Thereafter, the processrecursively returns to the caller (operation 2412) with the process thenreturning to operation 2400 as described above.

With reference again to operation 2408, if the parse tree is notcomplete, the process also returns to operation 2400. With referenceback to operation 2402, if an end of file has been reached, adetermination is made as to whether the grammar has been partiallyregenerated (operation 2414). This operation is performed to determinewhether incomplete statements or productions are present. Thisdetermination may be made by examining the parse trees to see ifincomplete parse trees are present. If the grammar is partiallyregenerated, an error is generated (operation 2416) with the processterminating thereafter. Otherwise, the process terminates withoutgenerating an error.

Turning now to FIG. 25, a flowchart of a process for executingstatements for productions is depicted in accordance with anadvantageous embodiment. The process illustrated in FIG. 25 may beimplemented in a software component, such as execute module 1732 in FIG.17.

The process begins by performing semantic analysis on the set ofinstructions for a production (operation 2500). This operation isperformed to determine whether semantic errors occur in any instructionsfor a production. The set of instructions is one or more instructions inthese examples. Thereafter, a determination is made as to whether asemantic error is present (operation 2502).

If a semantic error is not present, the process executes the set ofinstructions (operation 2504) with the process terminating thereafter.If in operation 2502 a semantic error occurs, the error is reported(operation 2506) with the process terminating thereafter. In some cases,rather than terminating, the process may attempt to correct the error toallow for execution of the instructions.

Turning now to FIG. 26, a diagram illustrating a graphical userinterface (GUI) processor is depicted in accordance with an advantageousembodiment. GUI processor 2600 is a more detailed illustration of GUIprocessor 406 in FIG. 4. In this example, GUI processor 2600 includesencryption/decryption modules 2602, graphics module 2604, output module2606, input module 2608, and HBDL generator 2610.

In these illustrative examples, GUI processor 2600 executes statementsreceived from source code, such as source code 600 in FIG. 6. Inparticular, the statements include those from GUI language 606 in sourcecode 600 in FIG. 6. The actual code for generating the displays arefound in these sources, rather than being a separate application.

GUI processor 2600 executes the statements and receives user input. GUIprocessor 2600 receives encrypted interpreted HBDL (EIHBDL) 2612 from aninterpreter such as interpreter 1700 in FIG. 17. Encryption/decryptionmodule 2602 decrypts information to form interpreted HBDL (IHBDL) 2614,which is processed by graphics module 2604. In these examples, IHBDL2614 represents primitives or a set of statements that may be used togenerate the display for devices 2618.

Graphics module 2604 may generate the pixels for display in the devices2618 and send that data to output module 2606, which in turn, transmitsthe data as device 2616 to devices 2618. User input is received asdevice 2620 from devices 2618 by input module 2608. This module sendsthe device data to HBDL generator 2610, which represents this user inputin the form of HBDL 2622. HBDL 2622 is the user input written in HBDL.This input is encrypted by encryption/decryption module 2602 andreturned to the interpreter as encrypted HBDL (EHBDL) 2624.

In these illustrative examples, GUI processor 2600 executes on hardwarethat is close to devices 2618. In fact, in many cases a portion of GUIprocessor 2600 may actually execute on devices 2618 with another portionexecuting on a data processing system, such as a server. GUI processor2600 is located close to devices 2618 in a manner to reduce the use ofnetwork resources needed to present the data. Further, the placement ofGUI processor 2600 is made in these examples to reduce latency indisplaying data and receiving user input.

Turning now to FIG. 27, a flowchart of a diagram illustrating data flowthrough a graphical user interface (GUI) processor is depicted inaccordance with an advantageous embodiment. In this example, graphicsmodule 2700 is an example of graphics module 2604 in FIG. 26. Graphicsmodule 2700 receives interpreted HBDL in the form of primitives 2702 inthese examples. These primitives are the results of interpretation ofthe source code by the interpreter.

Graphics module 2700 processes these primitives to generate pixels forbitmaps and data identifying how the bitmaps may be manipulated ordisplayed. This information is sent as bitmap data 2704 to clientprocess 2706. In these examples, client process 2706 is a processexecuting on a device, such as those in devices 1918 in FIG. 19. Thisclient process performs the operations needed to display the bitmap dataon display 2708. In this manner, the graphics processing necessary torender images for display are executed by graphics module 2700. Clientprocess 2706 only displays the bitmap data provided and does not needthe different processes and processing power needed to render bitmapgraphics from primitives. With this division of processing, the devicesdisplaying the data do not need the different graphic processors andgraphics pipelines used in rendering graphics such as those used inworkstations.

As a result, graphics may be displayed on a number of different devicesthat normally do not have sufficient processing power to handle theprimitives. For example, client process 2706 and display 2708 may beimplemented in a mobile phone, personal digital assistant, or tablet PC.

Input device 2710 receives user input with respect to data beingdisplayed in display 2708. This user input may manipulate graphics, suchas selecting a button, entering data, or sending a command. When a usermodifies the displayed image through manipulating the bitmaps on display2708, the difference or changes in the modification of the image beingdisplayed are identified by client process 2706. These differences inthe image form difference data 2712, which is returned to HBDL generator2714.

HBDL generator 2714 is similar to HBDL generator 2610 in FIG. 26. HBDLgenerator 2210 identifies this change or delta in information andtranslates it into HBDL 2716 for transmission to the interpreter. HBDL2716 contains statements or code in the language of the source codemodule and may be used to make changes to the source code. Graphicsmodule 2700 uses primitives 2702 to generate pixels for the differentbitmaps.

Turning now to FIG. 28, a diagram illustrating manipulation of a displayis depicted in accordance with an advantageous embodiment. In thisillustrative example, display 2800 is an example of a display presentedat display 2708 in FIG. 27. Display 2800 is presented using bitmapsgenerated from primitives. In this example, bitmaps are used torepresent different components, such as slider 2802, and field 2804. Thebitmap data used to represent slider 2802 and field 2804 are sent withdata indicating how these bitmaps may be manipulated. In this example,slider 2802 may be moved through user input from position 2806 in FIG.28 to position 2900 within display 2902 in FIG. 29, which is a modifiedversion of display 2800. Further, a value, such as fifty may be enteredinto field 2804 as depicted in display 2902 in FIG. 29. These changes inthe bitmaps are returned in accordance with an advantageous embodimentto the GUI processor, which then generates an appropriate statement forthe source code based on these changes.

Turning now to FIG. 30, a flowchart of a process for identifying changesin bitmaps is depicted in accordance with an advantageous embodiment.The process illustrated in FIG. 30 is an example of the process that maybe implemented in a client process at a device, such as client process2706 in FIG. 27.

The process begins by monitoring for user input (operation 3000). Adetermination is then made as to whether user input is detected withrespect to the display (operation 3002). If user input is not detected,the process returns to operation 3000. Otherwise, a determination ismade as to whether the user input manipulates a control (operation3004). If the user input manipulates a control, the change made to thecontrol is identified in the bitmap (operation 3006). This difference orchange in the bitmap is sent back to the GUI processor (operation 3008)with the process then returning to operation 3000 to monitor foradditional user input. The data change may be the actual bitmap that ischanged or an identification of the change in position of the bitmap,depending on the particular implementation. Of course, other types ofchanges may be used depending on the embodiment.

With reference again to operation 3004, if the user input is not amanipulation of a control, a determination is made as to whether theuser input is an entry of data into a field (operation 3010). If theuser input is not an entry of data, the process returns to operation3000. Otherwise, the process proceeds to (operation 3006) to identifythe change made in the bitmap.

The particular decisions made with respect to user input, in theseexamples, are ones for identifying changes to fields and controls in adisplay, such as execute module 2100 in FIG. 21. A determination may befor any type of change to a bitmap of interest. For example, the changemay be whether a particular button has been selected or turned.

Turning now to FIG. 31, a flowchart of a process for handling differencedata is depicted in accordance with an advantageous embodiment. Theprocess illustrated in FIG. 31 may be implemented in a GUI processor,such as GUI processor 2600 in FIG. 26. In particular, the processillustrated in FIG. 31 may be implemented in HBDL generator 2714 in FIG.27.

The process begins by receiving the difference data from a clientprocess (operation 3100). In these examples, the difference datacontains the changes in a bitmap made through user input. The processthen identifies the user input based on the difference (operation 3102).This user input may be identified as, for example, a change of a sliderposition, an entry of data into a data field, or some other user input.The identification made in operation 3102 may be made by comparing theoriginal bitmap sent to the device with the changed bitmap. For example,the user input may be to change the timeliness of a human if thedifference is identified as a movement of a slider upwards along thistype of control. An example of this type of difference is illustrated inFIG. 21 with respect to execute module 2100.

Thereafter, the user input is converted into a format used by the sourcecode (operation 3104). In these examples, the user input is changed intoa HPDL format. The converted user input is sent to the interpreter(operation 3106) with the process terminating thereafter.

With reference now to FIG. 32, a diagram illustrating components for usein providing a human transparency paradigm is depicted in accordancewith an advantageous embodiment. In this illustrative example,simulation 3200 is executed using a framework, such as framework 400 inFIG. 4. In particular, simulation 3200 is executed through theinterpretation of the source code by an interpreter, such as interpreter404 in FIG. 4.

In this particular example, simulation 3200 includes artificialintelligence (AI) 3202, which represents a human within simulation 3200.This human being executed by artificial intelligence 3202 is a synthetichuman in these examples. The code for artificial intelligence 3202 isretrieved from definition 3204 in addition to other information that isused for simulation 3200. Definition 3204 is found in source code, suchas source code 402 in FIG. 4. Definition 3204 includes the definition ofthe synthetic human as well as other humans and the environment in whichthe humans are present for simulation 3200.

As results are generated during simulation 3200, these results are sentto communication modules 3206 as user input 3208. These communicationsmodules, in this example, are also found in an interpreter, such asinterpreter 404 in FIG. 4. Communication modules 3206 takes user input3208 from simulation 3200 and modifies or writes new definitions intodefinition 3204. This forms modified source code, which is then used bysimulation 3200 to produce additional results. Artificial intelligence3202 logs into the framework in the same manner as a live user in theseexamples.

Further, results 3210 from simulation 3200 are sent to device 3212 forpresentation to user 3214. In these examples, user 3214 is a real human.

The illustrative embodiments allow for the use of a human transparencyparadigm. In this paradigm, user input 3208, generated by artificialintelligence 3202 that is rewritten into definition 3204, may bereplaced with live user input from user 3214. In other words, user 3214may send user input 3216 to communication modules 3206 to modify orwrite new definitions into definition 3204 in place of user input 3208,generated by the synthetic human simulated through artificialintelligence 3202 within simulation 3200. In these examples, user 3214may be a subject matter expert, providing user input 3216. In theseexamples, user input 3216 is provided during simulation 3200. This userinput may be in response to results received and presented at device3212.

The synthetic human, simulated by artificial intelligence 3202,generates user input 3208 that is associated with unique identifier (UI)3218 for the synthetic human. User input 3208 is generated by artificialintelligence 3202 during simulation 3200. User input 3216 is associatedwith unique identifier 3218. User input 3208 is sent to communicationmodules 3206. Communication modules 3206 modify definition 3204 byadding new definitions or modifying current definitions using user input3208. Communication modules 3206 knows which portion of definition 3204to modify based on unique identifier 3218.

When user 3214 generates user input 3216, user input 3216 is received bycommunication modules 3206. In these illustrative examples, user input3216 also may include unique identifier 3218. In this manner,communication modules 3206 modifies definition 3204 for the synthetichuman associated with unique identifier 3218.

In this manner, user 3214 may replace the synthetic human simulatedthrough artificial intelligence 3202 within simulation 3200. User 3214may turn artificial intelligence 3202 on and off on the fly, based onrequests sent to communication modules 3206.

In initiating a replacement of user input 3208 with user input 3216,user 3214 at device 3212 sends a request to communication modules 3206.At this point, user 3214 is assumed to have logged on and have beenauthenticated by communication modules 3206. Communication modules 3206determines whether user 3214 is authorized to turn on and off artificialintelligence 3202. In other words, communication modules 3206 determineswhether user 3214 may replace the synthetic human. If user 3214 isauthorized, communication modules 3206 sets a flag to stop usingartificial intelligence 3202. In other words, functions for artificialintelligence 3202 are no longer called within simulation 3200.

At this point in time, user 3214 generates user input 3216 that includesunique identifier 3218. Depending on the particular implementation,unique identifier 3218 may be added by communication modules 3206 basedon recognizing that user 3214 sends user input at device 3212.

With reference now to FIG. 33, a flowchart of a process for replacing asynthetic human with a live human is depicted in accordance with anadvantageous embodiment. In this example, the process illustrated inFIG. 33 may be implemented in an interpreter, such as interpreter 404 inFIG. 4. In particular, the process may be implemented in communicationmodules within interpreter 404.

The process begins by receiving a request from a user to replace asynthetic human (operation 3300). Thereafter, a determination is made asto whether the user is authorized to replace the simulated human(operation 3302). In these examples, the determination may be made bycomparing the user with a list or database defining what users mayreplace synthetic humans during a simulation. For example, certain usersmay be subject matter experts in certain areas and allowed to replacesynthetic humans for those particular areas. For example, a particularuser may be a subject matter expert with respect to politics. That usermay be allowed to replace a synthetic human that is a politician in thesimulation. That subject matter user, however, may not be allowed toreplace a synthetic human that is a farmer or a soldier because thesubject matter expert does not have expertise in those areas.

The particular rules for what users may replace synthetic humans dependentirely on the particular implementation. If the user is authorized toreplace the synthetic human, the use of the artificial intelligence forthe synthetic human is turned off in the definitions (operation 3304).

Thereafter, the process waits for user input from the user (operation3306). When user input is received, a determination is made as towhether the user input is to write a new definition in the definitions(operation 3308). If the user input is to write a new definition, theuser input is formatted into a form for writing the new definition(operation 3310). Thereafter, the definition is written into the sourcecode (operation 3312) with the process then returning to operation 3306as described above.

With reference again to operation 3308, if the user input is not towrite a new definition, a determination is made as to whether the userinput is to turn on the artificial intelligence (operation 3314). If theuser input is not to turn on the artificial intelligence, the processreturns to operation 3306. Otherwise, the artificial intelligence isturned back on for use in simulating the synthetic human (operation3316) with the process terminating thereafter. Operation 3316 places thesynthetic human back in place in a simulation and removes the live humanfrom the simulation.

With reference again to operation 3302, if the user is not authorized toreplace the synthetic human, an error message is generated (operation3318) with the process terminating thereafter.

The simulations provided by the framework are not intended predict humanbehavior with one-hundred percent certainly, but are intended to provideprobabilities that may decisions or changes. The results fromsimulations provide guidance and predictions that are otherwise notpossible with out the simulations made by framework.

In the different advantageous embodiments, source code 600 may beimplemented using a language specifically designed to provide thedifferent features in definition 602, actions 604, and GUI language 606in FIG. 6. In other advantageous embodiments, source code 600 in FIG. 6also may include functions and features from other existing languages orprogramming methodologies.

In the different advantageous embodiments, artificial intelligencesystems may be used to implement portions of source code 600, such as,for example, definition 602 and/or actions 604 in FIG. 6. In someadvantageous embodiments, artificial intelligence, in the form of aneural network (or other forms of artificial intelligence), may be usedto simulate various objects. For example, a neural network may be usedto simulate a living or live object, such as a person or an animal.

The artificial intelligence may be, for example, conventional artificialintelligence in which machine learning characterized by formalism andstatistical analysis is used. Additionally, artificial intelligence maybe, for example, in the form of computational intelligence.Computational intelligence involves an iterative development orlearning. This type of artificial intelligence may learn based onempirical data. Examples of computational intelligence include, forexample, without limitation, neural networks, fuzzy logic, and geneticalgorithms. These programming techniques may be used to supplement orprovide additional features within source code 600 in FIG. 6. In otheradvantageous embodiments, source code 600 in FIG. 6 may be implementedusing an existing programming language in addition to or in conjunctionwith various programming techniques.

In one advantageous embodiment, a programming technique such as a neuralnetwork may be used to implement portions of source code 600 in FIG. 6.For example, portions or all of definition 602 in FIG. 6 may beimplemented using neural networks. A neural network is a mathematicalcomputational model based on biological neural networks. Neural networksprovide non-linear statistical data modeling pools and may be used tomodel complex relationships between inputs and outputs. Neural networksmay be employed to provide learning functions for various objects withindefinition 602 in FIG. 6.

In one example, neural network techniques may be used to providelearning features for different objects such as people, animals, orother suitable objects within definition 602 of source code 600 in FIG.6. In this type of example, NN denotes a neural network type variable.An example of declaration may be “NN n;”. This type of statementdeclares a neural network variable, in this example. Children, n.input,n.output, and n.hidden also may be created. These other variablesrepresent input, output, and hidden layers in the neural network. Theselayers allow a user to add neurons to the different layers. With thesedifferent layers, input neurons may be added as children to the inputneural network layer.

Turning to FIG. 34, statements 3400 and 3402 are examples of inputneurons. Section 3404 in statement 3400 declares “left operand” as aninput neuron for the neural network n. This input member also may beused to read the total number of input neurons. The value of the member“input” is incremented by one each time a new member is added. As aresult, the input value is the total number of input neurons. Thesestatements are examples of HBDL pseudo code that could be implementedusing C language. Other languages, such as, for example, C+ and/orObjective-C.

In these examples, input neuron variables may range from a value of zeroto a value of one. Each input neuron variable has a minimum range and amaximum range. These ranges allow a user to enter any value within therange. These values also may be normalized before use, depending on theparticular implementation.

With reference to FIG. 35, statements 3500 and 3502 are examples ofinput ranges defined for the input neuron left operand. In this example,statement 3500 defines a minimum value of minus ten and statement 3502defines a maximum value of ten.

With reference now to FIG. 36, a diagram of a statement for inputbehavior is depicted in accordance with an advantageous embodiment.Statement 3600 allows an input neuron to be modified before the inputneuron is used. In other words, an input neuron may have code attachedor associated with the neuron to allow manipulation of the user input bythat neuron. For example, the user input may be short and long. In thisexample, the neuron input behavior interprets short and long to evaluatebetween zero and one. Statement 3600 is an example of a code that may beused to attach this type of behavior to the input neuron.

Turning next to FIG. 37, a diagram illustrating an output declaration isdepicted in accordance with an advantageous embodiment. Statement 3700is an example of a statement used to add children to the output neuralnetwork layer.

With reference now to FIG. 38, a diagram illustrating statements foroutput ranges in a neural network is depicted in accordance with anadvantageous embodiment. In this example, statements 3800 and 3802 areexamples of ranges that may be set for an output neuron. A minimum andmaximum range is set by statements 3800 and 3802.

In this particular example, the minimum value is minus fifty while themaximum value is fifty for the output neuron. Further, a user mayconvert an implicit normalized output to a value within the specifiedranges. For example, an output of one may be converted to 50, while anoutput of 0.5 is converted to 0. Further, an output neuron also may beassociated with the code to manipulate the output.

Turning now to FIG. 39, a diagram illustrating a statement for modifyingoutput behavior is depicted in accordance with an advantageousembodiment. Statement 3900 is an example of code that may be associatedwith an output neuron. In this example, the user output may be low andhigh. With this particular example, the output behavior of the neuronmay interpret a value between zero and one to low and high.

Turning now to FIG. 40, a diagram illustrating statements for hiddenlayers is depicted in accordance with an advantageous embodiment.Statements 4000 and 4002 are examples of statements that may be used todeclare hidden layers of any neural network. A hidden layer orderfollows the order of hidden layer declarations, in these examples. Thevalue of a hidden layer variable specifies the number of neuronsassigned to a particular hidden layer. Statements 4000 and 4002 declaretwo hidden layers, in these examples. The first layer defined bystatement 4000 includes five neurons and the second layer declared instatement 4002 defines three neurons.

With reference now to FIG. 41, code 4100 illustrates a sample neuralnetwork member being used to specify a neural sample value. Differentsamples may be specified. Each input and output neuron includes “sample[int]” within the statements. When a neural network is completed, a usermay train and use the neural network.

With reference now to FIG. 42, example statements for training a neuralnetwork are depicted in accordance with an advantageous embodiment.Statements 4200, 4202, and 4204 are examples of statements used toperform neural network training. Statement 4200 indicates that theneural network will be trained 500 times. Statement 4202 indicates 300times, and statement 4204 indicates 200 times for the training. In theseexamples, the training is cumulative with results being stored. Thesedifferent results may be stored within definition 602 or source code 602in FIG. 6 for a particular object.

Turning now to FIG. 43, a diagram illustrating a compute function in aneural network is depicted in accordance with an advantageousembodiment. In this example, statement 4300 and statement 4302 providean example of statements used to have the input neuron perform afunction and return a result. Statement 4304 is an alternativeexpression of statements 4300 and 4302, in these examples.

With reference now to FIG. 44, a diagram illustrating an example of aneural network is depicted in accordance with an advantageousembodiment. In this example, code 4400 includes a definition of a neuralnetwork along with statements to train and execute the neural network.Input declarations are found in section 4402. Input ranges are found insections 4404 and 4406. Code associated with neurons are found instatements 4408 and 4410. Output ranges are found in section 4412 andbehavior for the output is found in statement 4414.

Hidden layers are defined in section 4416 and the functionality is foundin statement 4418. Samples may be found in section 4420 and statement4422 is an example of a training statement. Section 4424 illustratesexamples of statements used to operate the neural network. Statement4426 within section 4424 displays the results.

Turning now to FIG. 45, a diagram illustrating the results from theoperation of a neural network is depicted in accordance with anadvantageous embodiment. In this example, display 4500 is an example ofa display generated in response to a show statement 4426 from code 4400in FIG. 44.

In addition to neural networks, dynamic lists may be used to managevarious attributes and properties of objects within definition 602 inFIG. 6. Dynamic lists may be used to define characteristics, such ascharacteristics 804 in object 800 in FIG. 8 and characteristics 904 forobject 900 in FIG. 9.

For example, dynamic lists may be used to provide an identification ofcomponents, capabilities, characteristics, or other suitable parametersfor an object. For example, if an object is a car, a dynamic list may beused to identify components, such as wheels, engine, body, paint,transmission, windows, and other components. As components are added toor removed from a car, the list may be modified to identify thesechanges.

In the different advantageous embodiments, any variable may be used in alist. With a dynamic list, definitions are not constrained by having topredefine list sizes based on expected components or parameters.Instead, the list size may change as various parameters or componentsare added or removed from a particular definition.

With reference to FIG. 46, a diagram illustrating an example of a listis depicted in accordance with an advantageous embodiment. In thisexample, code 4600 defines a list one in statement 4600. Statements4602, 4604, and 4606 identify three variables with values in list one.In this example, list one acts as an array. Statement 4608 in code 4600is an example of a size function that returns a value identifying thesize of the array. In this example, statement 4608 returns a value ofthree.

Statements 4610 and 4612 are examples of statements used to search thelist in code 4600. Statement 4610 returns a value of two which isequivalent to true, while statement 4612 returns a value of zero whichis equivalent to false. These search functions in statements 4610 and4612 may be used to determine whether the list contains a certain value.If the certain value is present in the list, the index to that value inthe list is returned. Otherwise, a value of zero is returned.

Turning now to FIG. 47, a diagram illustrating deleting a variable fromthe list is depicted in accordance with an advantageous embodiment. Inthis example, code 4700 includes a list as defined in section 4702.Statement 4704 is a delete function that may be used to delete an itemfrom the list in code 4700. Statement 4704 searches the list todetermine whether a particular item is present in the list. If the itemis found, the item is removed from the list. Statement 4704 returns anindex identifying the item removed. Otherwise, statement 4704 returns azero. In this example, the value twenty-five is not present in the listin code 4700, a zero is returned, and no action is taken. In thisexample, items are deleted by identifying values.

With reference now to FIG. 48, a diagram of code for deleting items isdepicted in accordance with an advantageous embodiment. In this example,code 4800 defines a list in section 4802. Statements 4804 and 4806 arestatements used to delete items in the list based on index values. If astatement identifies an index value less than the list size, the item islocated in the list and deleted. The function then returns a value forthe deleted item. Otherwise, a zero is returned meaning that the itemwas not found in the list. Statement 4804 returns a zero because onlythree items are present in the list as defined in section 4802.Statement 4806 results in a twenty being returned with the item definedin statement 4808 being deleted.

With reference now to FIG. 49, a diagram illustrating code to manipulateitems in the list is depicted in accordance with an advantageousembodiment. In this example, code 4900 may be used to manipulate itemsin a list. In these examples, code 4900 contains push and pop functionsto use the list of the stack. Statement 4902 identifies the list forwhich manipulations are to be made. In this example, section 4904identifies three pushes made to the list. Statement 4906 illustrates apop made to the list. Statement 4906 returns a value from the itempopped from the front of the list.

These types of functions are similar to those used to manipulate stacksin computer systems. A push is used to push or move a particular item tothe top of the list. A pop is used to return a value for the item at thetop of the list. If a pop statement includes a value or parameter, thisstatement pops or returns values from the stack according to the valueof the parameter. In statement 4908, the item being popped is the itemthat has an index value of three, which is the third item in the list,in these examples.

Turning now to FIG. 50, a diagram illustrating the use of the list as aqueue is depicted in accordance with an advantageous embodiment. In thisexample, code 5000 illustrates enqueue and dequeue functions that may beused to manipulate a list as a queue. An enqueue function, such as thatshown in statement 5002, adds an argument to the bottom of the list.

A dequeue function, such as that shown in statement 5004, removes thetop item from the list and returns a value of that item. In thisexample, statement 5002 adds an item to list one, statement 5006 addsanother item to the top of the queue. The item in statement 5002 is nowsecond in the queue. Statement 5008 adds yet another item to the queuepushing the other items lower into the queue.

Turning now to FIG. 51, a diagram illustrating reading items in the listis depicted in accordance with an advantageous embodiment. In thisexample, code 5100 illustrates reading items from the top and bottom ofthe list as defined in section 5102. Statement 5104 reads the itemlocated at the top of the list and statement 5106 reads the itemcontained at the bottom of the list.

Turning now to FIG. 52, a diagram illustrating a sort attribute in alist is depicted in accordance with an advantageous embodiment. Code5200 contains statements used to identify the sorting status for a list.Statement 5202 identifies whether a sorting status is set for the list.If statement 5202 is set equal to true, then items are inserted into thelist according to their value. Statement 5204 identifies a sortingorder.

If statement 5204 is set equal to true, the list is sorted in descendingorder from smallest to largest. In this example, statement 5204 is setequal to false. As a result, items are added to the list in descendingorder as shown in section 5206. Further, additional statements may beused to sort the list in various orders.

Another example of a programming technique that may be used to implementsource code 600 in FIG. 6 is fuzzy logic. An example of a language usedto implement fuzzy logic is Prolog, which is a logic programminglanguage that may be used for fuzzy logic and artificial intelligenceprogramming.

In the depicted examples, the fuzzy logic system may be based on logicalstatements in which operands are terms taken from several sets. In oneexample, the sets may be, for example, fuel, distance, and speed. Fuelmay contain three terms, low, medium, and high. Distance may be near andfar. Speed may be low, medium, and high, in these examples. These setsmay be used to apply rules, such as if the fuel is low or the distanceis near, then the speed is low. Another rule is if the fuel is mediumand the distance is far, then the speed is medium. A third rule is ifthe fuel is high and the distance is far, then the speed is high. Withfuzzy logic, ranges may be set for the different members of the set.These ranges include a minimum and a maximum range.

Turning now to FIG. 53, an example of a fuzzy logic implementation usingfuel distance and speed is depicted in accordance with an advantageousembodiment. In this example, code 5300 defines these sets in section5302. The fuel integer, while distance and speed are floating variables.Section 5304 identifies a minimum and maximum range for the fuel asbeing between 0 and 100.

Starting and ending terms for the fuel are defined in section 5306. Thissection identifies the left and right edges of a fuzzy set. Sections5308, 5310, and 5312 identify terms for the fuel. Section 5308identifies a trapezoid term, section 5310 identifies a triangular term,and section 5312 identifies a bell curve term.

Similar definitions for distance are found in section 5314. The rulesfor the fuzzy logic are defined in section 5316, in this example.Section 5318 identifies the initial values for fuel and distance.Statement 5320 is used to compute the speed.

Another type of programming technique that may be used to simulatevarious objects within source code 600 in FIG. 6 involves evolutionarycomputation. Evolutionary computation is a type of artificialintelligence. One specific method or methodology is a genetic algorithm.This algorithm is a search technique used to identify solutions. Thistype of technique is considered a global search heuristic type oftechnique. With a genetic algorithm, genes may be declared as well aschromosomes. Fitness function, selection process, and recombinationfunctions also are specified using this type of technique.

Turning now to FIG. 54, a diagram illustrating the solving of anequation using a genetic algorithm is depicted in accordance with anadvantageous embodiment. In this example, code 5400 is used to solveequation 2X+3Y=20.

In this example, two genes are initialized in section 5402. These twogenes correspond to variables X and Y. Chromosomes are added to thegenes in section 5404. Code for a fitness function may be identified instatement 5406. Code for a selection function may be specified usingstatement 5408. Code for a recombination function may be specified instatement 5410.

The code for these functions may be implemented using any availablefunction for the particular statement. These selection processes areused to select the most appropriate or best fit chromosome as specifiedby a code. For example, a roulette wheel selection process may be used.With respect to a recombination function in statement 5410, thisstatement may be used to identify code to build a new generation ofchromosomes.

In one example, a binary variable cross-over method may be used.Statement 5412 specifies an error margin for the process and statement5414 calls an evolve function in code 5400. The evolution processidentified in statement 5414 stops whenever the error margin of the mostfit chromosome is less than the error specified in statement 5412, inthese examples. By executing the process as defined in code 5400, gene Xreturns the values of X that most fit the chromosome and gene Y returnsthe values of Y that most fit the chromosome.

Turning now to FIGS. 55A and 55B, a diagram illustrating code for anobject in source code is depicted in accordance with an advantageousembodiment. In this example, code 5500 is an example of a definition foran object in the form of a forest. Section 5502 identifies colors fortrees in the forest. Section 5504 in code 5500 identifies a grid for theforest. Section 5506 defines trees that may be present in the grid forthe forest. Section 5508 identifies a row of tree for the forest.Section 5510 is used to generate a patch of trees, which contains one ormore rows of trees as defined in section 5508. Section 5512 is anexample of code that may be used to present the forest. In theseexamples, lines 5514 and 5516 are translation statements used to providefor randomness within the forest. Other statements, such as, forexample, rotation and/or scale statements also may be used in additionto or in place of these statements.

In these examples, code 5500 is written using C language. Of course, anylanguage may be used to generate a definition for a forest. Further, thepresentation of a forest is the example of one object that may be foundin definition 602 in source code 600 in FIG. 6. Of course, code may begenerated using any language or for any object, depending on theparticular implementation.

With a framework for human behavioral modeling and simulationdevelopment, such as framework 400 in FIG. 4, many different simulationsare made possible with respect to interactions between humans. Forexample, the simulation of human behavior is especially useful intraining purposes. In training development and administration, anability to tailor a training program to trainees allows the trainees tolearn more from the training program. Currently, effective mechanism fortailoring a training program is to have a trainer on-site to readreactions and adjust training based on what the trainer sees.

Further, current training methodologies also use behavioral sciencemodels. These models are used by a trainer to compare behavior of atrainee and obtain an indication of what a particular trainee may do ina particular situation. These profiles, however, are general profiles. Atrainer observes an individual trainee and determines what profile theindividual may fit into for use in predicting how the individual willreact to the training program. The different advantageous embodimentsrecognize that such a system is weak because the information forpredicting behavior is not based on the particular trainee, but on ageneralization of a type of personality or person.

Therefore, the advantageous embodiments provide a computer implementedmethod, apparatus, and computer usable program code human behaviormodeling for training purposes. The different embodiments provide anability to manage the training programs. In the illustrative examples,information about a set of trainees is identified for a trainingprogram. This set of trainees may be one or more trainees in theseexamples. A set of synthetic trainees is defined using the identifiedinformation about the set of trainees to form a first definition. Thisfirst definition is located in a source code in a framework used tosimulate human behavior. A training program is defined to form a seconddefinition in which the second definition also is located in the sourcecode for the framework. A simulation is performed for the set ofsynthetic trainees with the training program using the source code toobtain results.

The training program in the second definition may be modified based onthe results obtained in the simulation to form a modified trainingprogram in the second definition. The performing modifying steps may berepeated until desired results are obtained for the simulation. Indefining the training program, this training program may include boththe synthetic trainers and assets for the training program. Thesimulation also may be performed using live user input from a trainer,rather than using a synthetic trainer.

Turning now to FIG. 56, a diagram illustrating components used inmanaging a training program is depicted in accordance with anadvantageous embodiment. The managing of a training program in theseillustrative embodiments may be implemented in framework 400 in FIG. 4.

In this depicted example, training information 5600 and training program5602 are identified. Training information in these illustrativeexamples, is the information needed to create set of trainees 5604 inhumans 5606 within definition 5608 in source code 5610. Source code 5610is a component similar to source code 600 in FIG. 6. Humans 5606 issimilar to humans 704 in FIG. 7. Set of trainees 5604 in humans 5606 area group of synthetic humans for use in the simulation of trainingprogram 5602. The set of trainees is one or more trainees in theseexamples.

The information used to create the trainees in group of trainees 5604may be obtained from a number of different sources. The information maybe obtained using historical information about the trainee or directlycollecting the information about the trainee or using differenttechniques. This information may be gathered from questionnaires, teststaken by the trainees, observations made of the trainees. Thequestionnaires answered may include technical and personality surveys.Further, the trainers and/or psychological experts may make observationsfor use in defining the trainees within set of trainees 5604.

In these illustrative examples, a trainee generated in set of trainees5604 may include various attributes, such as, for example, ethnic,cultural, moral, religious, educational, and other factors that mayinfluence training effectiveness in performing the simulation.

Further, training program 5602 contains the information also may includeinformation about a set of trainers that may be used to train thetrainees. The set of trainers is one or more trainers in these examples.This information is used to create set of trainers 5612.

Training program 5602 is used to define training environment 5614 withinassets 5616. Assets 5616 are similar to assets 702 in FIG. 7. Assets5616 and humans 5606 are part of definition 5614. Definition 5614 issimilar to definition 700 in FIG. 7.

Interpreter 5618 uses source code 5610 to perform training simulation5618. In these examples, interpreter 5618 may be implemented usinginterpreter 1700 in FIG. 17. Interpreter 5618 generates results 5622,which are presented to a user. These results may be used to modify thetraining program or to determine whether further simulation is needed.

User input 5624 may be used to supply live user input from a set oftrainers rather than using the synthetic trainers in set of trainers5612 in humans 5606. Depending on the implementation, trainingsimulation 5620 may be performed entirely with live user input throughuser input 5624. Alternatively, the synthetic trainers in set oftrainers 5616 may be initially used with a switch during a portion ofthe simulation of the training program to live trainers through userinput 5624. The switch also may be made from live trainers to thesynthetic trainers in set of trainers 5612. These changes may be on thefly using the human transparency paragon as described above.

In this manner, simulations may be made to modify and improve thetraining program further, results 5622 may be used to select aparticular grouping of trainees that are found to be most effective. Forexample, set of trainees 5604 may be divided into three groups and theparticular make of each group may be optimized through performingtraining simulation 5620. Also, particular trainers may be selected forparticular groupings of trainees depending on results 5622. Thedifferent components illustrated in FIG. 56 do not include all of thecomponents in a framework, such as framework 400 in FIG. 4. Only some ofthe components are illustrated and described in order to more clearlydescribe the embodiments.

Turning now to FIG. 57, a flowchart of a process for managing a trainingprogram is depicted in accordance within an advantageous embodiment. Theprocess illustrated in FIG. 57 may be implemented in a framework, suchas framework 400 in FIG. 4.

The process begins by receiving information on trainees (operation5700). Thereafter, the process creates the synthetic humans representinga set of trainees in the definitions (operation 5702).

Thereafter, assets are created for the training program in thedefinitions (operation 5704). Trainers are created in the definition ofhumans (operation 5706). Operations 5700 through 5706 may be performedby an interpreter, such as interpreter 5618 in FIG. 56. In theseexamples, the information is received and created for the synthetichumans through user input to an interpreter, such as interpreter 5618 inFIG. 56. Depending on the particular implementation, the information maybe directly written into the source code rather than using theinterpreter.

Thereafter, the simulation of the training program is initiated(operation 5708). The process then generates results (operation 5710).

Thereafter, a determination is made as to whether the training programshould be modified (operation 5712). This determination may be made byan artificial intelligence component that is part of the simulation.Alternatively, the determination may be made by a user viewing theresults. If the training program is to be modified, the definitions ofassets and humans are then modified (operation 5714) with the processthen returning to operation 5706). Otherwise, the process terminates. Asin operation 5714, the modification of definitions and assets may bemade by an artificial intelligence program making changes to improve theprocess. Alternatively, these modifications may be made by a live userviewing the results of the simulation.

Turning now to FIG. 58, a flowchart of a process for managing a trainingprogram is depicted in accordance with an advantageous embodiment. Theprocess illustrated in FIG. 58 may be implemented in an interpreter,such as interpreter 5618 in FIG. 56.

The process begins by receiving information on trainees (operation5800). Thereafter, a group of trainees is generated (operation 5802).This group of trainees may be selected based on user input or by anartificial intelligence program in the interpreter. Thereafter,synthetic humans are created for the training group in the definition(operation 5804). Thereafter, assets are created for the trainingprogram (operation 5806).

The simulation is then initiated using the source code containing thedefinitions (operation 5808). Thereafter, user input is received from aset of subject matter experts (operation 5810). This set of subjectmatter experts in these examples is one or more trainers. Next, thedefinitions in the source code are modified using the received userinput (operation 5812). The simulation is then continued with themodified source code (operation 5814).

A determination is then made as to whether the simulation is complete(operation 5816). This determination may be made through user input.Alternatively, the determination in operation 5816 may be made based onwhether certain criteria are met. If the simulation is not complete, theprocess returns to operation 5810 otherwise, results are generated(operation 5818) with the process terminating thereafter.

Results in operation 5818 may be presented to users in a number ofdifferent ways. For example, the results may merely be end result of thetraining for the group of trainees. These results may contain the amountof learning that the different trainees achieved. Alternatively, theresults may include in addition to or in place of these results amodified training program that provides an improvement over the initialprogram.

Further, by simulating group dynamics for specific people rather thanusing a model or profile of a type of person, the results of thesimulations may be used to select particular individuals for a group oftrainees. Further, with being able to see the results of group dynamics,particular trainers also may be selected for particular groups oftrainees. Also, this simulation system may be used to train trainers onhow to react to a specific mix of personalities in a group of trainees.For example, a live trainer may see what happens and how the trainershould work with trainees. In this manner, multiple personalities may besimulated also with their interactions to identify the best trainingprogram for a particular group. In addition, this user of the frameworkmay be used to identify the makeup of groups that will provide for thebest learning experience in the training program.

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatus, methods and computer programproducts. In this regard, each block in the flowchart or block diagramsmay represent a module, segment, or portion of computer usable orreadable program code, which comprises one or more executableinstructions for implementing the specified function or functions. Insome alternative implementations, the function or functions noted in theblock may occur out of the order noted in the figures. For example, insome cases, two blocks shown in succession may be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

The different advantageous embodiments can take the form of an entirelyhardware embodiment, an entirely software embodiment, or an embodimentcontaining both hardware and software elements. Some embodiments areimplemented in software, which includes but is not limited to forms,such as, for example, firmware, resident software, and microcode.

Furthermore, the different embodiments can take the form of a computerprogram product accessible from a computer-usable or computer-readablemedium providing program code for use by or in connection with acomputer or any device or system that executes instructions. For thepurposes of this disclosure, a computer-usable or computer readablemedium can generally be any tangible apparatus that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.

The computer usable or computer readable medium can be, for example,without limitation an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, or a propagation medium. Non limitingexamples of a computer-readable medium include a semiconductor or solidstate memory, magnetic tape, a removable computer diskette, a randomaccess memory (RAM), a read-only memory (ROM), a rigid magnetic disk,and an optical disk. Optical disks may include compact disk-read onlymemory (CD-ROM), compact disk-read/write (CD-R/W) and DVD. In theseexamples, a physical or tangible computer readable medium is referred toas a recordable computer storage medium.

Further, a computer-usable or computer-readable medium may contain orstore a computer readable or usable program code such that when thecomputer readable or usable program code is executed on a computer, theexecution of this computer readable or usable program code causes thecomputer to transmit another computer readable or usable program codeover a communications link. This communications link may use a mediumthat is, for example without limitation, physical or wireless.

A data processing system suitable for storing and/or executing computerreadable or computer usable program code will include one or moreprocessors coupled directly or indirectly to memory elements through acommunications fabric, such as a system bus. The memory elements mayinclude local memory employed during actual execution of the programcode, bulk storage, and cache memories which provide temporary storageof at least some computer readable or computer usable program code toreduce the number of times code may be retrieved from bulk storageduring execution of the code.

Input/output or I/O devices can be coupled to the system either directlyor through intervening I/O controllers. These devices may include, forexample, without limitation to keyboards, touch screen displays, andpointing devices. Different communications adapters may also be coupledto the system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Non-limiting examplesare modems and network adapters are just a few of the currentlyavailable types of communications adapters.

The description of the present disclosure has been presented forpurposes of illustration and description, and is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art. Further, different advantageous embodiments may providedifferent advantages as compared to other advantageous embodiments. Theembodiment or embodiments selected are chosen and described in order tobest explain the principles of the invention, the practical application,and to enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated.

1. A computer implemented method for managing training programs, thecomputer implemented method comprising: identifying, using a set ofprocessors, information about a set of human trainees for a humantraining program to form identified information; defining, using the setof processors, a set of synthetic trainees using the identifiedinformation to form a first definition, wherein the first definition islocated in a source code in a framework used to simulate human behavior;defining, using the set of processors, the human training program toform a second definition, wherein the second definition is located inthe source code for the framework; performing, using the set ofprocessors, a simulation on the set of synthetic trainees with thesecond definition in the framework using the source code to obtainresults; and storing the results in a non-transitory medium incommunication with the set of processors.
 2. The computer implementedmethod of claim 1 further comprising: modifying, using the set ofprocessors, the human training program in the second definition based onthe results obtained from the simulation to form a modified trainingprogram in the second definition.
 3. The computer implemented method ofclaim 2 further comprising: repeating, using the set of processors, theperforming and modifying steps until desired results are obtained fromthe simulation.
 4. The computer implemented method of claim 2 furthercomprising: using the modified training program to train the set ofhuman trainees.
 5. The computer implemented method of claim 4, whereinthe step of performing the simulation comprises: training, using the setof processors, the set of synthetic trainees using the training program.6. The computer implemented method of claim 1, wherein the step ofdefining the human training program comprises: defining, using the setof processors, a set of synthetic trainers in the second definition; anddefining, using the set of processors, assets for the training programin the second definition.
 7. The computer implemented method of claim 1further comprising: performing, using the set of processors, thesimulation using live user input from a trainer.
 8. The computerimplemented method of claim 1 further comprising: using the results ofthe simulation to select a set of human trainers to train the set ofhuman trainees.
 9. The computer implemented method of claim 1 furthercomprising: using the results of the simulation to divide the set oftrainees into a plurality of groups.
 10. The computer implemented methodof claim 1, wherein the information comprises the information gatheredfrom questionnaires answered by the set of human trainees, tests takenby the set of human trainees, and observations made of the set of humantrainees.
 11. The computer implemented method of claim 1, wherein theinformation about the set of human trainees includes at least one ofethics information, cultural information, moral information, religiousinformation, education information, psychological, and physicalinformation about the set of human trainees.
 12. An apparatuscomprising: source code located on a storage system in a network dataprocessing system, wherein the source code is written in a language forpredicting human behavior, has a first definition for a set of humantrainees, and a second definition for a human training program; aninterpreter executing in the network data processing system, wherein theinterpreter executes a simulation of the human training program usingthe first definition and the second definition in source code togenerate interpreted source code for results of the simulation; and agraphical user interface processor executing in the network dataprocessing system, wherein the graphical user interface processorreceives the interpreted source code from the interpreter to formreceived interpreted source code and generates device dependent outputusing the received interpreted source code to present the results,wherein the interpreter uses the interpreted source code to modify thesource code to form modified source code for use during simulation ofthe human training program.
 13. The apparatus of claim 12, whereinresults are used to modify the first definition, with respect to the setof human trainees, in the source code to form a modified firstdefinition and wherein the modified first definition is used in asubsequent simulation of the human training program.
 14. The apparatusof claim 12, wherein the interpreter uses live user input from a user tomodify the source code to form the modified source code.
 15. A computerprogram product comprising: a non-transitory computer usable mediumhaving computer usable program code for managing human trainingprograms, the non-transitory computer program medium comprising:computer usable program code for identifying information about a set ofhuman trainees for a human training program to form identifiedinformation; computer usable program code for defining a set ofsynthetic trainees using the identified information to form a firstdefinition, wherein the first definition is located in a source code ina framework used to simulate human behavior; computer usable programcode for defining the human training program to form a seconddefinition, wherein the second definition is located in the source codefor the framework; and computer usable program code for performing asimulation on the set of synthetic trainees with the second definitionin the framework using the source code to obtain results.
 16. Thecomputer program product of claim 15 further comprising: computer usableprogram code for modifying the human training program in the seconddefinition based on the results obtained from the simulation to form amodified human training program in the second definition.
 17. Thecomputer program product of claim 16 further comprising: computer usableprogram code for using the modified human training program having thedesired results from the simulation to train the set of human trainees.18. The computer program product of claim 15 further comprising:computer usable program code for repeating the performing and modifyingsteps until desired results are obtained from the simulation.
 19. Thecomputer program product of claim 15, wherein the computer usableprogram code for defining a human training program to form a seconddefinition comprises: computer usable program code for defining a set ofsynthetic trainers in the second definition; and computer usable programcode for defining assets for the human training program in the seconddefinition.
 20. The computer program product of claim 15 furthercomprising: computer usable program code for performing the simulationusing live user input from a trainer.
 21. The computer program productof claim 15 further comprising: computer usable program code for usingthe results of the simulation to select a set of trainers to train theset of trainees.